Package 'crmPack' reference manual

Title:	Object-Oriented Implementation of CRM Designs
Description:	Implements a wide range of model-based dose escalation designs, ranging from classical and modern continual reassessment methods (CRMs) based on dose-limiting toxicity endpoints to dual-endpoint designs taking into account a biomarker/efficacy outcome. The focus is on Bayesian inference, making it very easy to setup a new design with its own JAGS code. However, it is also possible to implement 3+3 designs for comparison or models with non-Bayesian estimation. The whole package is written in a modular form in the S4 class system, making it very flexible for adaptation to new models, escalation or stopping rules.
Authors:	Daniel Sabanes Bove [aut, cre], Wai Yin Yeung [aut], Giuseppe Palermo [aut], Thomas Jaki [aut]
Maintainer:	Daniel Sabanes Bove <[email protected]>
License:	GPL (>= 2)
Version:	1.0.6
Built:	2024-11-24 06:35:01 UTC
Source:	CRAN

Object-oriented implementation of CRM designs

Description

Object-oriented implementation of CRM designs

Author(s)

Daniel Sabanes Bove [email protected], Wai Yin Yeung [email protected], Giuseppe Palermo [email protected], Thomas Jaki [email protected]

References

Sabanes Bove D, Yeung WY, Palermo G, Jaki T (2019). "Model-Based Dose Escalation Designs in R with crmPack." Journal of Statistical Software, 89(10), 1-22. doi:10.18637/jss.v089.i10 (URL: http://doi.org/10.18637/jss.v089.i10).

The method combining two atomic stopping rules

Description

The method combining two atomic stopping rules

Usage

## S4 method for signature 'Stopping,Stopping'
e1 & e2
## S4 method for signature 'Stopping,Stopping'
e1 & e2

Arguments

`e1`	First `Stopping` object
`e2`	Second `Stopping` object

Value

The StoppingAll object

Examples


## Example of combining two atomic stopping rules with an AND ('&') operator

myStopping1 <- StoppingMinCohorts(nCohorts=3)
myStopping2 <- StoppingTargetProb(target=c(0.2, 0.35),
                                  prob=0.5)

myStopping <- myStopping1 & myStopping2 




## Example of combining two atomic stopping rules with an AND ('&') operator

myStopping1 <- StoppingMinCohorts(nCohorts=3)
myStopping2 <- StoppingTargetProb(target=c(0.2, 0.35),
                                  prob=0.5)

myStopping <- myStopping1 & myStopping2

The method combining an atomic and a stopping list

Description

The method combining an atomic and a stopping list

Usage

## S4 method for signature 'Stopping,StoppingAll'
e1 & e2
## S4 method for signature 'Stopping,StoppingAll'
e1 & e2

Arguments

`e1`	`Stopping` object
`e2`	`StoppingAll` object

Value

The modified StoppingAll object

Examples


## Example of combining an atomic stopping rule with a list of stopping rules
## with an AND ('&') operator

myStopping1 <- StoppingMinCohorts(nCohorts=3)
myStopping2 <- StoppingTargetProb(target=c(0.2, 0.35),
                                  prob=0.5)

myStopping3 <- StoppingMinPatients(nPatients=20)

myStopping <-  myStopping3 & (myStopping1 | myStopping2 ) 




## Example of combining an atomic stopping rule with a list of stopping rules
## with an AND ('&') operator

myStopping1 <- StoppingMinCohorts(nCohorts=3)
myStopping2 <- StoppingTargetProb(target=c(0.2, 0.35),
                                  prob=0.5)

myStopping3 <- StoppingMinPatients(nPatients=20)

myStopping <-  myStopping3 & (myStopping1 | myStopping2 )

The method combining a stopping list and an atomic

Description

The method combining a stopping list and an atomic

Usage

## S4 method for signature 'StoppingAll,Stopping'
e1 & e2
## S4 method for signature 'StoppingAll,Stopping'
e1 & e2

Arguments

`e1`	`StoppingAll` object
`e2`	`Stopping` object

Value

The modified StoppingAll object

Examples


## Example of combining a list of stopping rules with an atomic stopping rule
## with an AND ('&') operator

myStopping1 <- StoppingMinCohorts(nCohorts=3)
myStopping2 <- StoppingTargetProb(target=c(0.2, 0.35),
                                  prob=0.5)

myStopping3 <- StoppingMinPatients(nPatients=20)

myStopping <- (myStopping1 | myStopping2 ) & myStopping3 




## Example of combining a list of stopping rules with an atomic stopping rule
## with an AND ('&') operator

myStopping1 <- StoppingMinCohorts(nCohorts=3)
myStopping2 <- StoppingTargetProb(target=c(0.2, 0.35),
                                  prob=0.5)

myStopping3 <- StoppingMinPatients(nPatients=20)

myStopping <- (myStopping1 | myStopping2 ) & myStopping3

Class for All models This is a class where all models inherit.

Description

Class for All models This is a class where all models inherit.

Slots

datanames: The names of all data slots that are used in all models. In particularly, those are also used in the datamodel and/or priormodel definition for GeneralModel.

Approximate posterior with (log) normal distribution

Description

It is recommended to use set.seed before, in order to be able to reproduce the resulting approximating model exactly.

Usage

approximate(object, model, data, ...)

## S4 method for signature 'Samples'
approximate(
  object,
  model,
  data,
  points = seq(from = min(data@doseGrid), to = max(data@doseGrid), length = 5L),
  refDose = median(points),
  logNormal = FALSE,
  verbose = TRUE,
  ...
)
approximate(object, model, data, ...)

## S4 method for signature 'Samples'
approximate(
  object,
  model,
  data,
  points = seq(from = min(data@doseGrid), to = max(data@doseGrid), length = 5L),
  refDose = median(points),
  logNormal = FALSE,
  verbose = TRUE,
  ...
)

Arguments

`object`	the `Samples` object
`model`	the `Model` object
`data`	the `Data` object
`...`	additional arguments (see methods)
`points`	optional parameter, which gives the dose values at which the approximation should rely on (default: 5 values equally spaced from minimum to maximum of the dose grid)
`refDose`	the reference dose to be used (default: median of `points`)
`logNormal`	use the log-normal prior? (not default) otherwise, the normal prior for the logistic regression coefficients is used
`verbose`	be verbose (progress statements and plot)? (default)

Value

the approximation model

Functions

approximate(Samples): Here the ... argument can transport additional arguments for Quantiles2LogisticNormal, e.g. in order to control the approximation quality, etc.

Examples


# Create some data
data <- Data(x = c(0.1, 0.5, 1.5, 3, 6, 10, 10, 10),
             y = c(0, 0, 0, 0, 0, 0, 1, 0),
             cohort = c(0, 1, 2, 3, 4, 5, 5, 5),
             doseGrid = c(0.1, 0.5, 1.5, 3, 6,
                          seq(from = 10, to = 80, by=2)))

# Initialize a model 
model <- LogisticLogNormal(mean = c(-0.85, 1),
                           cov = matrix(c(1, -0.5, -0.5, 1), nrow = 2),
                           refDose = 56)

# Get posterior for all model parameters
options <- McmcOptions(burnin = 100,
                       step = 2,
                       samples = 2000)
set.seed(94)
samples <- mcmc(data, model, options)

# Approximate the posterior distribution with a bivariate normal
# max.time and maxit are very small only for the purpose of showing the example. They 
# should be increased for a real case.
set.seed(94)
posterior <- approximate(object = samples,
                         model = model,
                         data = data,
                         logNormal=TRUE,
                         control = list(threshold.stop = 0.1,
                                        max.time = 1,
                                        maxit = 1))



# Create some data
data <- Data(x = c(0.1, 0.5, 1.5, 3, 6, 10, 10, 10),
             y = c(0, 0, 0, 0, 0, 0, 1, 0),
             cohort = c(0, 1, 2, 3, 4, 5, 5, 5),
             doseGrid = c(0.1, 0.5, 1.5, 3, 6,
                          seq(from = 10, to = 80, by=2)))

# Initialize a model 
model <- LogisticLogNormal(mean = c(-0.85, 1),
                           cov = matrix(c(1, -0.5, -0.5, 1), nrow = 2),
                           refDose = 56)

# Get posterior for all model parameters
options <- McmcOptions(burnin = 100,
                       step = 2,
                       samples = 2000)
set.seed(94)
samples <- mcmc(data, model, options)

# Approximate the posterior distribution with a bivariate normal
# max.time and maxit are very small only for the purpose of showing the example. They 
# should be increased for a real case.
set.seed(94)
posterior <- approximate(object = samples,
                         model = model,
                         data = data,
                         logNormal=TRUE,
                         control = list(threshold.stop = 0.1,
                                        max.time = 1,
                                        maxit = 1))

as.list method for the "GeneralData" class

Description

as.list method for the "GeneralData" class

Usage

## S4 method for signature 'GeneralData'
as.list(x, ...)
## S4 method for signature 'GeneralData'
as.list(x, ...)

Arguments

`x`	the `GeneralData` object we want to convert
`...`	unused

Value

a list of all slots in x

Examples


# Create some data of class 'Data'
myData <- Data(x=c(0.1,0.5,1.5,3,6,10,10,10),
               y=c(0,0,0,0,0,0,1,0),
               doseGrid=c(0.1,0.5,1.5,3,6,
               seq(from=10,to=80,by=2)))

# Converting Data object to list
as.list(myData)
# Create some data of class 'Data'
myData <- Data(x=c(0.1,0.5,1.5,3,6,10,10,10),
               y=c(0,0,0,0,0,0,1,0),
               doseGrid=c(0.1,0.5,1.5,3,6,
               seq(from=10,to=80,by=2)))

# Converting Data object to list
as.list(myData)

Compute the biomarker level for a given dose, given model and samples

Description

Compute the biomarker level for a given dose, given model and samples

Usage

biomLevel(dose, model, samples, ...)

## S4 method for signature 'numeric,DualEndpoint,Samples'
biomLevel(dose, model, samples, xLevel, ...)
biomLevel(dose, model, samples, ...)

## S4 method for signature 'numeric,DualEndpoint,Samples'
biomLevel(dose, model, samples, xLevel, ...)

Arguments

`dose`	the dose
`model`	the `DualEndpoint` object
`samples`	the `Samples` object
`...`	unused
`xLevel`	the grid index of `dose`

Functions

biomLevel(dose = numeric, model = DualEndpoint, samples = Samples): Here it is very easy, we just return the corresponding column (index xLevel) of the biomarker samples matrix, since we save that in the samples

Examples


# Create the data
data <- DataDual(
  x=c(0.1, 0.5, 1.5, 3, 6, 10, 10, 10,
      20, 20, 20, 40, 40, 40, 50, 50, 50),
  y=c(0, 0, 0, 0, 0, 0, 1, 0,
      0, 1, 1, 0, 0, 1, 0, 1, 1),
  w=c(0.31, 0.42, 0.59, 0.45, 0.6, 0.7, 0.55, 0.6,
      0.52, 0.54, 0.56, 0.43, 0.41, 0.39, 0.34, 0.38, 0.21),
  doseGrid=c(0.1, 0.5, 1.5, 3, 6,
             seq(from=10, to=80, by=2)))

# Initialize the Dual-Endpoint model (in this case RW1)
model <- DualEndpointRW(mu = c(0, 1),
                        Sigma = matrix(c(1, 0, 0, 1), nrow=2),
                        sigma2betaW = 0.01,
                        sigma2W = c(a=0.1, b=0.1),
                        rho = c(a=1, b=1),
                        smooth = "RW1")

# Set-up some MCMC parameters and generate samples from the posterior
options <- McmcOptions(burnin=100,
                       step=2,
                       samples=500)
set.seed(94)
samples <- mcmc(data, model, options)

# Obtain the biomarker level for a given dose, given model and samples
biomLevel(dose = 0.5,
          model = model,
          samples = samples,
          xLevel = 2)



# Create the data
data <- DataDual(
  x=c(0.1, 0.5, 1.5, 3, 6, 10, 10, 10,
      20, 20, 20, 40, 40, 40, 50, 50, 50),
  y=c(0, 0, 0, 0, 0, 0, 1, 0,
      0, 1, 1, 0, 0, 1, 0, 1, 1),
  w=c(0.31, 0.42, 0.59, 0.45, 0.6, 0.7, 0.55, 0.6,
      0.52, 0.54, 0.56, 0.43, 0.41, 0.39, 0.34, 0.38, 0.21),
  doseGrid=c(0.1, 0.5, 1.5, 3, 6,
             seq(from=10, to=80, by=2)))

# Initialize the Dual-Endpoint model (in this case RW1)
model <- DualEndpointRW(mu = c(0, 1),
                        Sigma = matrix(c(1, 0, 0, 1), nrow=2),
                        sigma2betaW = 0.01,
                        sigma2W = c(a=0.1, b=0.1),
                        rho = c(a=1, b=1),
                        smooth = "RW1")

# Set-up some MCMC parameters and generate samples from the posterior
options <- McmcOptions(burnin=100,
                       step=2,
                       samples=500)
set.seed(94)
samples <- mcmc(data, model, options)

# Obtain the biomarker level for a given dose, given model and samples
biomLevel(dose = 0.5,
          model = model,
          samples = samples,
          xLevel = 2)

The virtual class for cohort sizes

Description

The virtual class for cohort sizes

Initialization function for "CohortSizeConst"

Description

Initialization function for "CohortSizeConst"

Usage

CohortSizeConst(size)
CohortSizeConst(size)

Arguments

size

see CohortSizeConst

Value

the CohortSizeConst object

Constant cohort size

Description

This class is used when the cohort size should be kept constant.

Slots

size: the constant integer size

Examples


# This is to have along the study a constant cohort size of 3
mySize <- CohortSizeConst(size=3)

# This is to have along the study a constant cohort size of 3
mySize <- CohortSizeConst(size=3)

Initialization function for "CohortSizeDLT"

Description

Initialization function for "CohortSizeDLT"

Usage

CohortSizeDLT(DLTintervals, cohortSize)
CohortSizeDLT(DLTintervals, cohortSize)

Arguments

`DLTintervals`	see `CohortSizeDLT`
`cohortSize`	see `CohortSizeDLT`

Value

the CohortSizeDLT object

Cohort size based on number of DLTs

Description

Cohort size based on number of DLTs

Slots

DLTintervals: an integer vector with the left bounds of the relevant DLT intervals
cohortSize: an integer vector of the same length with the cohort sizes in the DLTintervals

Examples


# As example, here is the rule for: 
#   having cohort of size 1 until no DLT were observed
#   and having cohort of size 3 as soon as 1 DLT is observed

mySize <- CohortSizeDLT(DLTintervals = c(0, 1),
                        cohortSize = c(1, 3))

# As example, here is the rule for: 
#   having cohort of size 1 until no DLT were observed
#   and having cohort of size 3 as soon as 1 DLT is observed

mySize <- CohortSizeDLT(DLTintervals = c(0, 1),
                        cohortSize = c(1, 3))

Initialization function for "CohortSizeMax"

Description

Initialization function for "CohortSizeMax"

Usage

CohortSizeMax(cohortSizeList)
CohortSizeMax(cohortSizeList)

Arguments

cohortSizeList

see CohortSizeMax

Value

the CohortSizeMax object

Size based on maximum of multiple cohort size rules

Description

This class can be used to combine multiple cohort size rules with the MAX operation.

Details

cohortSizeList contains all cohort size rules, which are again objects of class CohortSize. The maximum of these individual cohort sizes is taken to give the final cohort size.

Slots

cohortSizeList: list of cohort size rules

Examples


# Rule for having cohort of size 1 for doses <30
#      and having cohort of size 3 for doses >=30
mySize1 <- CohortSizeRange(intervals = c(0, 10),
                           cohortSize = c(1, 3))

# Rule for having cohort of size 1 until no DLT were observed
#      and having cohort of size 3 as soon as 1 DLT is observed
mySize2 <- CohortSizeDLT(DLTintervals=c(0, 1),
                         cohortSize=c(1, 3))

# Create a list of cohort size rules of class 'CohortSizeMax' which will then be 
# combined with the 'max' operation
mySize <- CohortSizeMax(cohortSizeList=list(mySize1, 
                                            mySize2))


# Rule for having cohort of size 1 for doses <30
#      and having cohort of size 3 for doses >=30
mySize1 <- CohortSizeRange(intervals = c(0, 10),
                           cohortSize = c(1, 3))

# Rule for having cohort of size 1 until no DLT were observed
#      and having cohort of size 3 as soon as 1 DLT is observed
mySize2 <- CohortSizeDLT(DLTintervals=c(0, 1),
                         cohortSize=c(1, 3))

# Create a list of cohort size rules of class 'CohortSizeMax' which will then be 
# combined with the 'max' operation
mySize <- CohortSizeMax(cohortSizeList=list(mySize1, 
                                            mySize2))

Initialization function for "CohortSizeMin"

Description

Initialization function for "CohortSizeMin"

Usage

CohortSizeMin(cohortSizeList)
CohortSizeMin(cohortSizeList)

Arguments

cohortSizeList

see CohortSizeMin

Value

the CohortSizeMin object

Size based on minimum of multiple cohort size rules

Description

This class can be used to combine multiple cohort size rules with the MIN operation.

Details

cohortSizeList contains all cohort size rules, which are again objects of class CohortSize. The minimum of these individual cohort sizes is taken to give the final cohort size.

Slots

cohortSizeList: list of cohort size rules

Examples


# Rule for having cohort of size 1 for doses <30
#      and having cohort of size 3 for doses >=30
mySize1 <- CohortSizeRange(intervals = c(0, 10),
                           cohortSize = c(1, 3))

# Rule for having cohort of size 1 until no DLT were observed
#      and having cohort of size 3 as soon as 1 DLT is observed
mySize2 <- CohortSizeDLT(DLTintervals=c(0, 1),
                         cohortSize=c(1, 3))

# Create a list of cohort size rules of class 'CohortSizeMax' which will then be 
# combined with the 'min' operation
mySize <- CohortSizeMin(cohortSizeList=list(mySize1, 
                                            mySize2))


# Rule for having cohort of size 1 for doses <30
#      and having cohort of size 3 for doses >=30
mySize1 <- CohortSizeRange(intervals = c(0, 10),
                           cohortSize = c(1, 3))

# Rule for having cohort of size 1 until no DLT were observed
#      and having cohort of size 3 as soon as 1 DLT is observed
mySize2 <- CohortSizeDLT(DLTintervals=c(0, 1),
                         cohortSize=c(1, 3))

# Create a list of cohort size rules of class 'CohortSizeMax' which will then be 
# combined with the 'min' operation
mySize <- CohortSizeMin(cohortSizeList=list(mySize1, 
                                            mySize2))

Initialization function for "CohortSizeParts"

Description

Initialization function for "CohortSizeParts"

Usage

CohortSizeParts(sizes)
CohortSizeParts(sizes)

Arguments

sizes

see CohortSizeParts

Value

the CohortSizeParts object

Cohort size based on the parts

Description

This class is used when the cohort size should change for the second part of the dose escalation. Only works in conjunction with DataParts objects.

Slots

sizes: the two sizes for part 1 and part 2

Examples


mySize <- CohortSizeParts(sizes=c(1,3))

mySize <- CohortSizeParts(sizes=c(1,3))

Initialization function for "CohortSizeRange"

Description

Initialization function for "CohortSizeRange"

Usage

CohortSizeRange(intervals, cohortSize)
CohortSizeRange(intervals, cohortSize)

Arguments

`intervals`	see `CohortSizeRange`
`cohortSize`	see `CohortSizeRange`

Value

the CohortSizeRange object

Cohort size based on dose range

Description

Cohort size based on dose range

Slots

intervals: a vector with the left bounds of the relevant dose intervals
cohortSize: an integer vector of the same length with the cohort sizes in the intervals

Examples


# As example, here is the rule for: 
#   having cohort of size 1 for doses <30
#   and having cohort of size 3 for doses >=30

mySize <- CohortSizeRange(intervals=c(0, 30),
                          cohortSize=c(1, 3))

# As example, here is the rule for: 
#   having cohort of size 1 for doses <30
#   and having cohort of size 3 for doses >=30

mySize <- CohortSizeRange(intervals=c(0, 30),
                          cohortSize=c(1, 3))

Open the example pdf for crmPack

Description

Calling this helper function should open the example.pdf document, residing in the doc subfolder of the package installation directory.

Usage

crmPackExample()
crmPackExample()

Value

nothing

Author(s)

Daniel Sabanes Bove [email protected]

Open the browser with help pages for crmPack

Description

This convenience function opens your browser with the help pages for crmPack.

Usage

crmPackHelp()
crmPackHelp()

Value

nothing

Author(s)

Daniel Sabanes Bove [email protected]

Initialization function for the "Data" class

Description

This is the function for initializing a "Data" class object.

Usage

Data(
  x = numeric(),
  y = integer(),
  ID = integer(),
  cohort = integer(),
  doseGrid = numeric(),
  placebo = FALSE,
  ...
)
Data(
  x = numeric(),
  y = integer(),
  ID = integer(),
  cohort = integer(),
  doseGrid = numeric(),
  placebo = FALSE,
  ...
)

Arguments

`x`	the doses for the patients
`y`	the vector of toxicity events (0 or 1 integers). You can also normal numeric vectors, but these will then be converted to integers.
`ID`	unique patient IDs (integer vector)
`cohort`	the cohort indices (sorted values from 0, 1, 2, ...)
`doseGrid`	the vector of all possible doses
`placebo`	logical value: if TRUE the first dose level in the grid is considered as PLACEBO
`...`	not used

Details

Note that ID and cohort can be missing, then a warning will be issued and the variables will be filled with default IDs and best guesses, respectively.

Value

the initialized Data object

Class for the data input

Description

This class inherits from GeneralData.

Slots

x: the doses for the patients
y: the vector of toxicity events (0 or 1 integers)
doseGrid: the vector of all possible doses (sorted), i.e. the dose grid
nGrid: number of gridpoints
xLevel: the levels for the doses the patients have been given
placebo: logical value: if TRUE the first dose level in the grid is considered as PLACEBO

Examples

# create some data from the class 'Data'
myData <- Data(x=c(0.1,0.5,1.5,3,6,10,10,10),
               y=c(0,0,0,0,0,0,1,0),
               doseGrid=c(0.1,0.5,1.5,3,6,
                          seq(from=10,to=80,by=2)))
# create some data from the class 'Data'
myData <- Data(x=c(0.1,0.5,1.5,3,6,10,10,10),
               y=c(0,0,0,0,0,0,1,0),
               doseGrid=c(0.1,0.5,1.5,3,6,
                          seq(from=10,to=80,by=2)))

Initialization function for the "DataDual" class

Description

This is the function for initializing a "DataDual" class object.

Usage

DataDual(w = numeric(), ...)
DataDual(w = numeric(), ...)

Arguments

`w`	the continuous vector of biomarker values
`...`	additional parameters from `Data`

Value

the initialized DataDual object

Class for the dual endpoint data input

Description

This is a subclass of Data, so contains all slots from Data, and in addition biomarker values.

Slots

w: the continuous vector of biomarker values

Examples

# Create some data from the class 'DataDual'
myData <- DataDual(x=c(0.1,0.5,1.5,3,6,10,10,10),
                   y=c(0,0,0,0,0,0,1,0),
                   w=rnorm(8),
                   doseGrid=c(0.1,0.5,1.5,3,6,
                              seq(from=10,to=80,by=2)))
# Create some data from the class 'DataDual'
myData <- DataDual(x=c(0.1,0.5,1.5,3,6,10,10,10),
                   y=c(0,0,0,0,0,0,1,0),
                   w=rnorm(8),
                   doseGrid=c(0.1,0.5,1.5,3,6,
                              seq(from=10,to=80,by=2)))

Initialization function for the "DataMixture" class

Description

This is the function for initializing a "DataMixture" class object.

Usage

DataMixture(xshare = numeric(), yshare = integer(), ...)
DataMixture(xshare = numeric(), yshare = integer(), ...)

Arguments

`xshare`	see `DataMixture`
`yshare`	see `DataMixture`
`...`	additional arguments for the underlying Data slots

Value

the initialized DataMixture object

Class for the data with mixture sharing

Description

Class for the data with mixture sharing

Slots

xshare: the doses for the share patients
yshare: the vector of toxicity events (0 or 1 integers) for the share patients
nObsshare: number of share patients

Examples

## decide on the dose grid:
doseGrid <- 1:80

## and MCMC options:
options <- McmcOptions()

## the classic model would be:
model <- LogisticLogNormal(mean = c(-0.85, 1),
                           cov = matrix(c(1, -0.5, -0.5, 1), nrow = 2),
                           refDose = 50)

nodata <- Data(doseGrid=doseGrid)

priorSamples <- mcmc(nodata, model, options)
plot(priorSamples, model, nodata)

## set up the mixture model and data share object:
modelShare <- LogisticLogNormalMixture(shareWeight=0.1,
                                       mean = c(-0.85, 1),
                                       cov = matrix(c(1, -0.5, -0.5, 1), nrow = 2),
                                       refDose = 50)

nodataShare <- DataMixture(doseGrid=doseGrid,
                           xshare=
                             c(rep(10, 4),
                               rep(20, 4),
                               rep(40, 4)),
                           yshare=
                             c(rep(0L, 4),
                               rep(0L, 4),
                               rep(0L, 4)))

## now compare with the resulting prior model:
priorSamplesShare <- mcmc(nodataShare, modelShare, options)
plot(priorSamplesShare, modelShare, nodataShare)
## decide on the dose grid:
doseGrid <- 1:80

## and MCMC options:
options <- McmcOptions()

## the classic model would be:
model <- LogisticLogNormal(mean = c(-0.85, 1),
                           cov = matrix(c(1, -0.5, -0.5, 1), nrow = 2),
                           refDose = 50)

nodata <- Data(doseGrid=doseGrid)

priorSamples <- mcmc(nodata, model, options)
plot(priorSamples, model, nodata)

## set up the mixture model and data share object:
modelShare <- LogisticLogNormalMixture(shareWeight=0.1,
                                       mean = c(-0.85, 1),
                                       cov = matrix(c(1, -0.5, -0.5, 1), nrow = 2),
                                       refDose = 50)

nodataShare <- DataMixture(doseGrid=doseGrid,
                           xshare=
                             c(rep(10, 4),
                               rep(20, 4),
                               rep(40, 4)),
                           yshare=
                             c(rep(0L, 4),
                               rep(0L, 4),
                               rep(0L, 4)))

## now compare with the resulting prior model:
priorSamplesShare <- mcmc(nodataShare, modelShare, options)
plot(priorSamplesShare, modelShare, nodataShare)

Initialization function for the "DataParts" class

Description

This is the function for initializing a DataParts object.

Usage

DataParts(part = integer(), nextPart = 1L, part1Ladder = numeric(), ...)
DataParts(part = integer(), nextPart = 1L, part1Ladder = numeric(), ...)

Arguments

`part`	which part does each of the patients belong to?
`nextPart`	what is the part for the next cohort? (1 or 2)
`part1Ladder`	what is the escalation ladder for part 1?
`...`	additional parameters from `Data`

Value

the initialized DataParts object

Class for the data with two study parts

Description

This is a subclass of Data, so contains all slots from Data, and in addition information on the two study parts.

Slots

part: integer vector; which part does each of the patients belong to?
nextPart: integer; what is the part for the next cohort?
part1Ladder: sorted numeric vector; what is the escalation ladder for part 1? This shall be a subset of the doseGrid.

Examples

# create an object of class 'DataParts'
myData <- DataParts(x=c(0.1,0.5,1.5),
                    y=c(0,0,0),
                    doseGrid=c(0.1,0.5,1.5,3,6,
                               seq(from=10,to=80,by=2)),
                    part=c(1L,1L,1L),
                    nextPart=1L,
                    part1Ladder=c(0.1,0.5,1.5,3,6,10))
# create an object of class 'DataParts'
myData <- DataParts(x=c(0.1,0.5,1.5),
                    y=c(0,0,0),
                    doseGrid=c(0.1,0.5,1.5,3,6,
                               seq(from=10,to=80,by=2)),
                    part=c(1L,1L,1L),
                    nextPart=1L,
                    part1Ladder=c(0.1,0.5,1.5,3,6,10))

Initialization function for "Design"

Description

Initialization function for "Design"

Usage

Design(model, stopping, increments, PLcohortSize = CohortSizeConst(0L), ...)
Design(model, stopping, increments, PLcohortSize = CohortSizeConst(0L), ...)

Arguments

`model`	see `Design`
`stopping`	see `Design`
`increments`	see `Design`
`PLcohortSize`	see `Design`
`...`	additional arguments for `RuleDesign`

Value

the Design object

Class for the CRM design

Description

In addition to the slots in the more simple RuleDesign, objects of this class contain:

Slots

model: the model to be used, an object of class Model
stopping: stopping rule(s) for the trial, an object of class Stopping
increments: how to control increments between dose levels, an object of class Increments
PLcohortSize: rules for the cohort sizes for placebo, if any planned an object of class CohortSize (defaults to constant 0 placebo patients)

Examples


# Define the dose-grid
emptydata <- Data(doseGrid = c(1, 3, 5, 10, 15, 20, 25, 40, 50, 80, 100))

# Initialize the CRM model 
model <- LogisticLogNormal(mean=c(-0.85, 1),
                           cov=
                             matrix(c(1, -0.5, -0.5, 1),
                                    nrow=2),
                           refDose=56)

# Choose the rule for selecting the next dose 
myNextBest <- NextBestNCRM(target=c(0.2, 0.35),
                           overdose=c(0.35, 1),
                           maxOverdoseProb=0.25)

# Choose the rule for the cohort-size 
mySize1 <- CohortSizeRange(intervals=c(0, 30),
                           cohortSize=c(1, 3))
mySize2 <- CohortSizeDLT(DLTintervals=c(0, 1),
                         cohortSize=c(1, 3))
mySize <- maxSize(mySize1, mySize2)

# Choose the rule for stopping
myStopping1 <- StoppingMinCohorts(nCohorts=3)
myStopping2 <- StoppingTargetProb(target=c(0.2, 0.35),
                                  prob=0.5)
myStopping3 <- StoppingMinPatients(nPatients=20)
myStopping <- (myStopping1 & myStopping2) | myStopping3

# Choose the rule for dose increments
myIncrements <- IncrementsRelative(intervals=c(0, 20),
                                   increments=c(1, 0.33))

# Initialize the design
design <- Design(model=model,
                 nextBest=myNextBest,
                 stopping=myStopping,
                 increments=myIncrements,
                 cohortSize=mySize,
                 data=emptydata,
                 startingDose=3)
  
  
# Define the dose-grid
emptydata <- Data(doseGrid = c(1, 3, 5, 10, 15, 20, 25, 40, 50, 80, 100))

# Initialize the CRM model 
model <- LogisticLogNormal(mean=c(-0.85, 1),
                           cov=
                             matrix(c(1, -0.5, -0.5, 1),
                                    nrow=2),
                           refDose=56)

# Choose the rule for selecting the next dose 
myNextBest <- NextBestNCRM(target=c(0.2, 0.35),
                           overdose=c(0.35, 1),
                           maxOverdoseProb=0.25)

# Choose the rule for the cohort-size 
mySize1 <- CohortSizeRange(intervals=c(0, 30),
                           cohortSize=c(1, 3))
mySize2 <- CohortSizeDLT(DLTintervals=c(0, 1),
                         cohortSize=c(1, 3))
mySize <- maxSize(mySize1, mySize2)

# Choose the rule for stopping
myStopping1 <- StoppingMinCohorts(nCohorts=3)
myStopping2 <- StoppingTargetProb(target=c(0.2, 0.35),
                                  prob=0.5)
myStopping3 <- StoppingMinPatients(nPatients=20)
myStopping <- (myStopping1 & myStopping2) | myStopping3

# Choose the rule for dose increments
myIncrements <- IncrementsRelative(intervals=c(0, 20),
                                   increments=c(1, 0.33))

# Initialize the design
design <- Design(model=model,
                 nextBest=myNextBest,
                 stopping=myStopping,
                 increments=myIncrements,
                 cohortSize=mySize,
                 data=emptydata,
                 startingDose=3)

Compute the doses for a given probability, given model and samples

Description

Compute the doses for a given probability, given model and samples

Usage

dose(prob, model, samples, ...)

## S4 method for signature 'numeric,Model,Samples'
dose(prob, model, samples, ...)

## S4 method for signature 'numeric,ModelTox,Samples'
dose(prob, model, samples, ...)

## S4 method for signature 'numeric,ModelTox,missing'
dose(prob, model, samples, ...)
dose(prob, model, samples, ...)

## S4 method for signature 'numeric,Model,Samples'
dose(prob, model, samples, ...)

## S4 method for signature 'numeric,ModelTox,Samples'
dose(prob, model, samples, ...)

## S4 method for signature 'numeric,ModelTox,missing'
dose(prob, model, samples, ...)

Arguments

`prob`	the probability
`model`	the `Model`
`samples`	the `Samples`
`...`	unused

Functions

dose(prob = numeric, model = ModelTox, samples = Samples): Compute the doses for a given probability, given Pseudo DLE model with samples
dose(prob = numeric, model = ModelTox, samples = missing): Compute the dose for a given probability and a given Pseudo DLE model without samples

Examples


# create some data
data <- Data(x =c (0.1, 0.5, 1.5, 3, 6, 10, 10, 10),
             y = c(0, 0, 0, 0, 0, 0, 1, 0),
             cohort = c(0, 1, 2, 3, 4, 5, 5, 5),
             doseGrid = c(0.1, 0.5, 1.5, 3, 6,
                          seq(from=10, to=80, by=2)))

# Initialize a  model
model <- LogisticLogNormal(mean=c(-0.85, 1),
                           cov=matrix(c(1, -0.5, -0.5, 1),
                                      nrow=2),
                           refDose=56)

# Get samples from posterior
options <- McmcOptions(burnin=100,
                       step=2,
                       samples=2000)
set.seed(94)
samples <- mcmc(data, model, options)

# Posterior for the dose achieving Prob(DLE) = 0.45
TD45 <- dose(prob=0.45,model=model,samples=samples)


# create data from the 'Data" (or DataDual') class
data <- Data(x = c(25,50,25,50,75,300,250,150),
             y = c(0,0,0,0,0,1,1,0),
             doseGrid = seq(25,300,25))

## Initialize a model from 'ModelTox' class e.g using 'LogisticIndepBeta' model
DLEmodel <- LogisticIndepBeta(binDLE=c(1.05,1.8),
                              DLEweights=c(3,3),
                              DLEdose=c(25,300),
                              data=data)

options <- McmcOptions(burnin=100, step=2, samples=200)
DLEsamples <- mcmc(data=data,model=DLEmodel,options=options)

TD45 <- dose(prob=0.45, model = DLEmodel,samples = DLEsamples)


# create data from the 'Data' (or 'DataDual') class
data <- Data(x = c(25,50,25,50,75,300,250,150),
                 y = c(0,0,0,0,0,1,1,0),
                 doseGrid = seq(25,300,25))

## Initialize a model from 'ModelTox' class e.g using 'LogisticIndepBeta' model
DLEmodel <- LogisticIndepBeta(binDLE=c(1.05,1.8),
                              DLEweights=c(3,3),
                              DLEdose=c(25,300),
                              data=data)

TD45 <- dose(prob=0.45, model = DLEmodel)

# create some data
data <- Data(x =c (0.1, 0.5, 1.5, 3, 6, 10, 10, 10),
             y = c(0, 0, 0, 0, 0, 0, 1, 0),
             cohort = c(0, 1, 2, 3, 4, 5, 5, 5),
             doseGrid = c(0.1, 0.5, 1.5, 3, 6,
                          seq(from=10, to=80, by=2)))

# Initialize a  model
model <- LogisticLogNormal(mean=c(-0.85, 1),
                           cov=matrix(c(1, -0.5, -0.5, 1),
                                      nrow=2),
                           refDose=56)

# Get samples from posterior
options <- McmcOptions(burnin=100,
                       step=2,
                       samples=2000)
set.seed(94)
samples <- mcmc(data, model, options)

# Posterior for the dose achieving Prob(DLE) = 0.45
TD45 <- dose(prob=0.45,model=model,samples=samples)


# create data from the 'Data" (or DataDual') class
data <- Data(x = c(25,50,25,50,75,300,250,150),
             y = c(0,0,0,0,0,1,1,0),
             doseGrid = seq(25,300,25))

## Initialize a model from 'ModelTox' class e.g using 'LogisticIndepBeta' model
DLEmodel <- LogisticIndepBeta(binDLE=c(1.05,1.8),
                              DLEweights=c(3,3),
                              DLEdose=c(25,300),
                              data=data)

options <- McmcOptions(burnin=100, step=2, samples=200)
DLEsamples <- mcmc(data=data,model=DLEmodel,options=options)

TD45 <- dose(prob=0.45, model = DLEmodel,samples = DLEsamples)


# create data from the 'Data' (or 'DataDual') class
data <- Data(x = c(25,50,25,50,75,300,250,150),
                 y = c(0,0,0,0,0,1,1,0),
                 doseGrid = seq(25,300,25))

## Initialize a model from 'ModelTox' class e.g using 'LogisticIndepBeta' model
DLEmodel <- LogisticIndepBeta(binDLE=c(1.05,1.8),
                              DLEweights=c(3,3),
                              DLEdose=c(25,300),
                              data=data)

TD45 <- dose(prob=0.45, model = DLEmodel)

Initialization function for "DualDesign"

Description

Initialization function for "DualDesign"

Usage

DualDesign(model, data, ...)
DualDesign(model, data, ...)

Arguments

`model`	see `DualDesign`
`data`	see `DualDesign`
`...`	additional arguments for `Design`

Value

the DualDesign object

Class for the dual-endpoint CRM design

Description

This class has special requirements for the model and data slots in comparison to the parent class Design:

Slots

model

the model to be used, an object of class DualEndpoint

data

what is the dose grid, any previous data, etc., contained in an object of class DataDual

Note that the NextBest slot can be of any class, this allows for easy comparison with recommendation methods that don't use the biomarker information.

Examples


# Define the dose-grid
emptydata <- DataDual(doseGrid = c(1, 3, 5, 10, 15, 20, 25, 40, 50, 80, 100))

# Initialize the CRM model 
model <- DualEndpointRW(mu = c(0, 1),
                        Sigma = matrix(c(1, 0, 0, 1), nrow=2),
                        sigma2betaW = 0.01,
                        sigma2W = c(a=0.1, b=0.1),
                        rho = c(a=1, b=1),
                        smooth="RW1")

# Choose the rule for selecting the next dose 
myNextBest <- NextBestDualEndpoint(target=c(0.9, 1),
                                   overdose=c(0.35, 1),
                                   maxOverdoseProb=0.25)

# Choose the rule for the cohort-size 
mySize1 <- CohortSizeRange(intervals=c(0, 30),
                           cohortSize=c(1, 3))
mySize2 <- CohortSizeDLT(DLTintervals=c(0, 1),
                         cohortSize=c(1, 3))
mySize <- maxSize(mySize1, mySize2)

# Choose the rule for stopping
myStopping4 <- StoppingTargetBiomarker(target=c(0.9, 1),
                                       prob=0.5)
myStopping <- myStopping4 | StoppingMinPatients(40)

# Choose the rule for dose increments
myIncrements <- IncrementsRelative(intervals=c(0, 20),
                                   increments=c(1, 0.33))

# Initialize the design
design <- DualDesign(model = model,
                     data = emptydata,
                     nextBest = myNextBest,
                     stopping = myStopping,
                     increments = myIncrements,
                     cohortSize = mySize,
                     startingDose = 3)
  
  
# Define the dose-grid
emptydata <- DataDual(doseGrid = c(1, 3, 5, 10, 15, 20, 25, 40, 50, 80, 100))

# Initialize the CRM model 
model <- DualEndpointRW(mu = c(0, 1),
                        Sigma = matrix(c(1, 0, 0, 1), nrow=2),
                        sigma2betaW = 0.01,
                        sigma2W = c(a=0.1, b=0.1),
                        rho = c(a=1, b=1),
                        smooth="RW1")

# Choose the rule for selecting the next dose 
myNextBest <- NextBestDualEndpoint(target=c(0.9, 1),
                                   overdose=c(0.35, 1),
                                   maxOverdoseProb=0.25)

# Choose the rule for the cohort-size 
mySize1 <- CohortSizeRange(intervals=c(0, 30),
                           cohortSize=c(1, 3))
mySize2 <- CohortSizeDLT(DLTintervals=c(0, 1),
                         cohortSize=c(1, 3))
mySize <- maxSize(mySize1, mySize2)

# Choose the rule for stopping
myStopping4 <- StoppingTargetBiomarker(target=c(0.9, 1),
                                       prob=0.5)
myStopping <- myStopping4 | StoppingMinPatients(40)

# Choose the rule for dose increments
myIncrements <- IncrementsRelative(intervals=c(0, 20),
                                   increments=c(1, 0.33))

# Initialize the design
design <- DualDesign(model = model,
                     data = emptydata,
                     nextBest = myNextBest,
                     stopping = myStopping,
                     increments = myIncrements,
                     cohortSize = mySize,
                     startingDose = 3)

Initialization function for the "DualEndpoint" class

Description

Initialization function for the "DualEndpoint" class

Usage

DualEndpoint(mu, Sigma, refDose = 1, useLogDose = FALSE, sigma2W, rho)
DualEndpoint(mu, Sigma, refDose = 1, useLogDose = FALSE, sigma2W, rho)

Arguments

`mu`	see `DualEndpoint`
`Sigma`	see `DualEndpoint`
`refDose`	see `DualEndpoint` (default: 1)
`useLogDose`	see `DualEndpoint` (default: `FALSE`)
`sigma2W`	see `DualEndpoint`
`rho`	see `DualEndpoint`

Value

the DualEndpoint object

General class for the dual endpoint model

Description

The idea of the dual-endpoint models is to model not only the dose-toxicity relationship, but also to model at the same time the relationship of a PD biomarker with the dose. The subclasses of this class detail how the dose-biomarker relationship is parametrized and are those to be used. This class here shall contain all the common features to reduce duplicate code. (However, this class must not be virtual, because we need to create objects of it during the construction of subclass objects.)

Details

Currently a probit regression model

$probit[p(x)] = \beta_{Z1} + \beta_{Z2} \cdot x/x^{*}$

$probit[p(x)] = \beta_{Z1} + \beta_{Z2} \cdot \log(x/x^{*})$

in case that the option useLogDose is TRUE. Here $p(x)$ is the probability of observing a DLT for a given dose $x$ , $\Phi$ is the standard normal cdf, and $x^{*}$ is the reference dose.

The prior is

$\left( \beta_{Z1} , log(\beta_{Z2}) \right) \sim Normal(\mu, \Sigma)$

For the biomarker response w at a dose x, we assume

$w(x) \sim Normal(f(x), \sigma^{2}_{W})$

and $f(x)$ is a function of the dose x, which is further specified in the subclasses. The biomarker variance $\sigma^{2}_{W}$ can be fixed or assigned an inverse gamma prior distribution; see the details below under slot sigma2W.

Finally, the two endpoints y (the binary DLT variable) and w (the biomarker) can be correlated, by assuming a correlation $\rho$ between the underlying continuous latent toxicity variable z and the biomarker w. Again, this correlation can be fixed or assigned a prior distribution from the scaled beta family; see the details below under slot rho.

Please see the Hive page for more details on the model and the example vignette by typing crmPackExample() for a full example.

Slots

mu: For the probit toxicity model, mu contains the prior mean vector
Sigma: For the probit toxicity model, contains the prior covariance matrix
refDose: For the probit toxicity model, the reference dose
useLogDose: For the probit toxicity model, whether a log transformation of the (standardized) dose should be used?
sigma2W: Either a fixed value for the biomarker variance, or a vector with elements a and b for the inverse-gamma prior parameters.
rho: Either a fixed value for the correlation (between -1 and 1), or a vector with elements a and b for the Beta prior on the transformation kappa = (rho + 1) / 2, which is in (0, 1). For example, a=1,b=1 leads to a uniform prior on rho.
useFixed: a list with logical value for each of the parameters indicating whether a fixed value is used or not; this slot is needed for internal purposes and not to be touched by the user.

Initialization function for the "DualEndpointBeta" class

Description

Initialization function for the "DualEndpointBeta" class

Usage

DualEndpointBeta(E0, Emax, delta1, mode, refDoseBeta, ...)
DualEndpointBeta(E0, Emax, delta1, mode, refDoseBeta, ...)

Arguments

`E0`	see `DualEndpointBeta`
`Emax`	see `DualEndpointBeta`
`delta1`	see `DualEndpointBeta`
`mode`	see `DualEndpointBeta`
`refDoseBeta`	see `DualEndpointBeta`
`...`	additional parameters, see `DualEndpoint`

Value

the DualEndpointBeta object

Dual endpoint model with beta function for dose-biomarker relationship

Description

This class extends the DualEndpoint class. Here the dose-biomarker relationship $f(x)$ is modelled by a parametric, rescaled beta density function:

Details

$f(x) = E_{0} + (E_{max} - E_{0}) * Beta(\delta_{1}, \delta_{2}) * (x/x^{*})^{\delta_{1}} * (1 - x/x^{*})^{\delta_{2}}$

where $x^{*}$ is the maximum dose (end of the dose range to be considered), $\delta_{1}$ and $\delta_{2}$ are the two beta parameters, and $E_{0}$ and $E_{max}$ are the minimum and maximum levels, respectively. For ease of interpretation, we parametrize with $\delta_{1}$ and the mode of the curve instead, where

$mode = \delta_{1} / (\delta_{1} + \delta_{2}),$

and multiplying this with $x^{*}$ gives the mode on the dose grid.

All parameters can currently be assigned uniform distributions or be fixed in advance. Note that E0 and Emax can have negative values or uniform distributions reaching into negative range, while delta1 and mode must be positive or have uniform distributions in the positive range.

Slots

E0: either a fixed number or the two uniform distribution parameters
Emax: either a fixed number or the two uniform distribution parameters
delta1: either a fixed number or the two uniform distribution parameters
mode: either a fixed number or the two uniform distribution parameters
refDoseBeta: the reference dose $x^{*}$ (note that this is different from the refDose in the inherited DualEndpoint model)

Examples


model <- DualEndpointBeta(E0 = c(0, 100),
                          Emax = c(0, 500),
                          delta1 = c(0, 5),
                          mode = c(1, 15),
                          refDose=10,
                          useLogDose=TRUE,
                          refDoseBeta = 1000,
                          mu = c(0, 1),
                          Sigma = matrix(c(1, 0, 0, 1), nrow=2),
                          sigma2W = c(a=0.1, b=0.1),
                          rho = c(a=1, b=1))


model <- DualEndpointBeta(E0 = c(0, 100),
                          Emax = c(0, 500),
                          delta1 = c(0, 5),
                          mode = c(1, 15),
                          refDose=10,
                          useLogDose=TRUE,
                          refDoseBeta = 1000,
                          mu = c(0, 1),
                          Sigma = matrix(c(1, 0, 0, 1), nrow=2),
                          sigma2W = c(a=0.1, b=0.1),
                          rho = c(a=1, b=1))

Initialization function for the "DualEndpointEmax" class

Description

Initialization function for the "DualEndpointEmax" class

Usage

DualEndpointEmax(E0, Emax, ED50, refDoseEmax, ...)
DualEndpointEmax(E0, Emax, ED50, refDoseEmax, ...)

Arguments

`E0`	see `DualEndpointEmax`
`Emax`	see `DualEndpointEmax`
`ED50`	see `DualEndpointEmax`
`refDoseEmax`	see `DualEndpointEmax`
`...`	additional parameters, see `DualEndpoint`

Value

the DualEndpointEmax object

Dual endpoint model with emax function for dose-biomarker relationship

Description

This class extends the DualEndpoint class. Here the dose-biomarker relationship $f(x)$ is modelled by a parametric EMAX function:

Details

$f(x) = E_{0} + \frac{(E_{max} - E_{0}) * (x/x^{*})}{ED_{50} + (x/x^{*})}$

where $x^{*}$ is a reference dose, $E_{0}$ and $E_{max}$ are the minimum and maximum levels for the biomarker and $ED_{50}$ is the dose achieving half of the maximum effect $0.5 * E_{max}$ .

All parameters can currently be assigned uniform distributions or be fixed in advance.

Slots

E0: either a fixed number or the two uniform distribution parameters
Emax: either a fixed number or the two uniform distribution parameters
ED50: either a fixed number or the two uniform distribution parameters
refDoseEmax: the reference dose $x^{*}$

Examples


model <- DualEndpointEmax(E0 = c(0, 100),
                          Emax = c(0, 500),
                          ED50 = c(10,200),
                          refDoseEmax = 1000,
                          mu = c(0, 1),
                          Sigma = matrix(c(1, 0, 0, 1), nrow=2),
                          sigma2W = c(a=0.1, b=0.1),
                          rho = c(a=1, b=1))


model <- DualEndpointEmax(E0 = c(0, 100),
                          Emax = c(0, 500),
                          ED50 = c(10,200),
                          refDoseEmax = 1000,
                          mu = c(0, 1),
                          Sigma = matrix(c(1, 0, 0, 1), nrow=2),
                          sigma2W = c(a=0.1, b=0.1),
                          rho = c(a=1, b=1))

Initialization function for the "DualEndpointRW" class

Description

Initialization function for the "DualEndpointRW" class

Usage

DualEndpointRW(sigma2betaW, smooth = c("RW1", "RW2"), ...)
DualEndpointRW(sigma2betaW, smooth = c("RW1", "RW2"), ...)

Arguments

`sigma2betaW`	see `DualEndpointRW`
`smooth`	either “RW1” (default) or “RW2”, for specifying the random walk prior on the biomarker level.
`...`	additional parameters, see `DualEndpoint`

Value

the DualEndpointRW object

Dual endpoint model with RW prior for biomarker

Description

This class extends the DualEndpoint class. Here the dose-biomarker relationship $f(x)$ is modelled by a non-parametric random-walk of first (RW1) or second order (RW2).

Details

That means, for the RW1 we assume

$\beta_{W,i} - \beta_{W,i-1} \sim Normal(0, (x_{i} - x_{i-1}) \sigma^{2}_{\beta_{W}}),$

where $\beta_{W,i} = f(x_{i})$ is the biomarker mean at the i-th dose gridpoint $x_{i}$ . For the RW2, the second-order differences instead of the first-order differences of the biomarker means follow the normal distribution.

The variance parameter $\sigma^{2}_{\beta_{W}}$ is important because it steers the smoothness of the function f(x): if it is large, then f(x) will be very wiggly; if it is small, then f(x) will be smooth. This parameter can either be fixed or assigned an inverse gamma prior distribution.

Non-equidistant dose grids can be used now, because the difference $x_{i} - x_{i-1}$ is included in the modelling assumption above.

Please note that due to impropriety of the RW prior distributions, it is not possible to produce MCMC samples with empty data objects (i.e., sample from the prior). This is not a bug, but a theoretical feature of this model.

Slots

sigma2betaW: Contains the prior variance factor of the random walk prior for the biomarker model. If it is not a single number, it can also contain a vector with elements a and b for the inverse-gamma prior on sigma2betaW.
useRW1: for specifying the random walk prior on the biomarker level: if TRUE, RW1 is used, otherwise RW2.

Examples



model <- DualEndpointRW(mu = c(0, 1),
                        Sigma = matrix(c(1, 0, 0, 1), nrow=2),
                        sigma2betaW = 0.01,
                        sigma2W = c(a=0.1, b=0.1),
                        rho = c(a=1, b=1),
                        smooth="RW1")


model <- DualEndpointRW(mu = c(0, 1),
                        Sigma = matrix(c(1, 0, 0, 1), nrow=2),
                        sigma2betaW = 0.01,
                        sigma2W = c(a=0.1, b=0.1),
                        rho = c(a=1, b=1),
                        smooth="RW1")

Initialization function for 'DualResponsesDesign"

Description

Initialization function for 'DualResponsesDesign"

Usage

DualResponsesDesign(Effmodel, data, ...)
DualResponsesDesign(Effmodel, data, ...)

Arguments

`Effmodel`	please refer to `DualResponsesDesign` class object
`data`	please refer to `DualResponsesDesign` class object
`...`	additional arguments for `TDDesign`

Value

the DualResponsesDesign class object

This is a class of design based on DLE responses using the `LogisticIndepBeta` model model and efficacy responses using `ModelEff` model class without DLE and efficacy samples. It contain all slots in `RuleDesign` and `TDDesign` class object

Description

This is a class of design based on DLE responses using the LogisticIndepBeta model model and efficacy responses using ModelEff model class without DLE and efficacy samples. It contain all slots in RuleDesign and TDDesign class object

Slots

data: the data set of DataDual class object
Effmodel: the pseudo efficacy model to be used, an object class of ModelEff

Examples

##Construct the DualResponsesDesign for simulations
##The design comprises the DLE and efficacy models, the escalation rule, starting data, 
##a cohort size and a starting dose
##Define your data set first using an empty data set 
## with dose levels from 25 to 300 with increments 25
data <- DataDual(doseGrid=seq(25,300,25),placebo=FALSE)
##First for the DLE model 
##The DLE model must be of 'ModelTox' (e.g 'LogisticIndepBeta') class 
DLEmodel <- LogisticIndepBeta(binDLE=c(1.05,1.8),
                              DLEweights=c(3,3),
                              DLEdose=c(25,300),
                              data=data)

##The efficacy model of 'ModelEff' (e.g 'Effloglog') class 
Effmodel<-Effloglog(Eff=c(1.223,2.513),Effdose=c(25,300),
                    nu=c(a=1,b=0.025),data=data,c=0)

##The escalation rule using the 'NextBestMaxGain' class
mynextbest<-NextBestMaxGain(DLEDuringTrialtarget=0.35,
                            DLEEndOfTrialtarget=0.3)


##The increments (see Increments class examples) 
## 200% allowable increase for dose below 300 and 200% increase for dose above 300
myIncrements<-IncrementsRelative(intervals=c(25,300),
                                 increments=c(2,2))
##cohort size of 3
mySize<-CohortSizeConst(size=3)
##Stop only when 36 subjects are treated
myStopping <- StoppingMinPatients(nPatients=36)
##Now specified the design with all the above information and starting with a dose of 25

design <- DualResponsesDesign(nextBest=mynextbest,
                              model=DLEmodel,
                              Effmodel=Effmodel,
                              stopping=myStopping,
                              increments=myIncrements,
                              cohortSize=mySize,
                              data=data,startingDose=25)
##Construct the DualResponsesDesign for simulations
##The design comprises the DLE and efficacy models, the escalation rule, starting data, 
##a cohort size and a starting dose
##Define your data set first using an empty data set 
## with dose levels from 25 to 300 with increments 25
data <- DataDual(doseGrid=seq(25,300,25),placebo=FALSE)
##First for the DLE model 
##The DLE model must be of 'ModelTox' (e.g 'LogisticIndepBeta') class 
DLEmodel <- LogisticIndepBeta(binDLE=c(1.05,1.8),
                              DLEweights=c(3,3),
                              DLEdose=c(25,300),
                              data=data)

##The efficacy model of 'ModelEff' (e.g 'Effloglog') class 
Effmodel<-Effloglog(Eff=c(1.223,2.513),Effdose=c(25,300),
                    nu=c(a=1,b=0.025),data=data,c=0)

##The escalation rule using the 'NextBestMaxGain' class
mynextbest<-NextBestMaxGain(DLEDuringTrialtarget=0.35,
                            DLEEndOfTrialtarget=0.3)


##The increments (see Increments class examples) 
## 200% allowable increase for dose below 300 and 200% increase for dose above 300
myIncrements<-IncrementsRelative(intervals=c(25,300),
                                 increments=c(2,2))
##cohort size of 3
mySize<-CohortSizeConst(size=3)
##Stop only when 36 subjects are treated
myStopping <- StoppingMinPatients(nPatients=36)
##Now specified the design with all the above information and starting with a dose of 25

design <- DualResponsesDesign(nextBest=mynextbest,
                              model=DLEmodel,
                              Effmodel=Effmodel,
                              stopping=myStopping,
                              increments=myIncrements,
                              cohortSize=mySize,
                              data=data,startingDose=25)

Initialization function for 'DualResponsesSamplesDesign"

Description

Initialization function for 'DualResponsesSamplesDesign"

Usage

DualResponsesSamplesDesign(Effmodel, data, ...)
DualResponsesSamplesDesign(Effmodel, data, ...)

Arguments

`Effmodel`	please refer to `DualResponsesSamplesDesign` class object
`data`	please refer to `DualResponsesSamplesDesign` class object
`...`	additional arguments for `TDsamplesDesign`

Value

the DualResponsesSamplesDesign class object

This is a class of design based on DLE responses using the `LogisticIndepBeta` model model and efficacy responses using `ModelEff` model class with DLE and efficacy samples.It contain all slots in `RuleDesign` and `TDsamplesDesign` class object

Description

This is a class of design based on DLE responses using the LogisticIndepBeta model model and efficacy responses using ModelEff model class with DLE and efficacy samples.It contain all slots in RuleDesign and TDsamplesDesign class object

Slots

data: the data set of DataDual class object
Effmodel: the pseudo efficacy model to be used, an object class of ModelEff

Examples

##Construct the DualResponsesSamplesDesign for simulations
##The design comprises the DLE and efficacy models, the escalation rule, starting data, 
##a cohort size and a starting dose
##Define your data set first using an empty data set 
## with dose levels from 25 to 300 with increments 25
data <- DataDual(doseGrid=seq(25,300,25),placebo=FALSE)

## First for the DLE model and DLE samples
## The DLE model must be of 'ModelTox' 
## (e.g 'LogisticIndepBeta') class and 
## DLEsamples of 'Samples' class
options<-McmcOptions(burnin=100,step=2,samples=200)
DLEmodel <- LogisticIndepBeta(binDLE=c(1.05,1.8),DLEweights=c(3,3),
                              DLEdose=c(25,300),data=data)
DLEsamples<-mcmc(data,DLEmodel,options)
##The efficacy model of 'ModelEff' (e.g 'Effloglog') class and the efficacy samples
Effmodel<-Effloglog(Eff=c(1.223,2.513),Effdose=c(25,300),nu=c(a=1,b=0.025),data=data,c=0)
Effsamples<-mcmc(data,Effmodel,options)
##The escalation rule using the 'NextBestMaxGainSamples' class
mynextbest<-NextBestMaxGainSamples(DLEDuringTrialtarget=0.35,
                                   DLEEndOfTrialtarget=0.3,
                                   TDderive=function(TDsamples){
                                     quantile(TDsamples,prob=0.3)},
                                   Gstarderive=function(Gstarsamples){
                                     quantile(Gstarsamples,prob=0.5)})


##The increments (see Increments class examples) 
## 200% allowable increase for dose below 300 and 200% increase for dose above 300
myIncrements<-IncrementsRelative(intervals=c(25,300),
                                 increments=c(2,2))
##cohort size of 3
mySize<-CohortSizeConst(size=3)
##Stop only when 36 subjects are treated
myStopping <- StoppingMinPatients(nPatients=36)
##Now specified the design with all the above information and starting with a dose of 25

design <- DualResponsesSamplesDesign(nextBest=mynextbest,
                                     cohortSize=mySize,
                                     startingDose=25,
                                     model=DLEmodel,
                                     Effmodel=Effmodel,
                                     data=data,
                                     stopping=myStopping,
                                     increments=myIncrements)

##Construct the DualResponsesSamplesDesign for simulations
##The design comprises the DLE and efficacy models, the escalation rule, starting data, 
##a cohort size and a starting dose
##Define your data set first using an empty data set 
## with dose levels from 25 to 300 with increments 25
data <- DataDual(doseGrid=seq(25,300,25),placebo=FALSE)

## First for the DLE model and DLE samples
## The DLE model must be of 'ModelTox' 
## (e.g 'LogisticIndepBeta') class and 
## DLEsamples of 'Samples' class
options<-McmcOptions(burnin=100,step=2,samples=200)
DLEmodel <- LogisticIndepBeta(binDLE=c(1.05,1.8),DLEweights=c(3,3),
                              DLEdose=c(25,300),data=data)
DLEsamples<-mcmc(data,DLEmodel,options)
##The efficacy model of 'ModelEff' (e.g 'Effloglog') class and the efficacy samples
Effmodel<-Effloglog(Eff=c(1.223,2.513),Effdose=c(25,300),nu=c(a=1,b=0.025),data=data,c=0)
Effsamples<-mcmc(data,Effmodel,options)
##The escalation rule using the 'NextBestMaxGainSamples' class
mynextbest<-NextBestMaxGainSamples(DLEDuringTrialtarget=0.35,
                                   DLEEndOfTrialtarget=0.3,
                                   TDderive=function(TDsamples){
                                     quantile(TDsamples,prob=0.3)},
                                   Gstarderive=function(Gstarsamples){
                                     quantile(Gstarsamples,prob=0.5)})


##The increments (see Increments class examples) 
## 200% allowable increase for dose below 300 and 200% increase for dose above 300
myIncrements<-IncrementsRelative(intervals=c(25,300),
                                 increments=c(2,2))
##cohort size of 3
mySize<-CohortSizeConst(size=3)
##Stop only when 36 subjects are treated
myStopping <- StoppingMinPatients(nPatients=36)
##Now specified the design with all the above information and starting with a dose of 25

design <- DualResponsesSamplesDesign(nextBest=mynextbest,
                                     cohortSize=mySize,
                                     startingDose=25,
                                     model=DLEmodel,
                                     Effmodel=Effmodel,
                                     data=data,
                                     stopping=myStopping,
                                     increments=myIncrements)

Initialization function for "DualSimulations"

Description

Initialization function for "DualSimulations"

Usage

DualSimulations(rhoEst, sigma2West, fitBiomarker, ...)
DualSimulations(rhoEst, sigma2West, fitBiomarker, ...)

Arguments

`rhoEst`	see `DualSimulations`
`sigma2West`	see `DualSimulations`
`fitBiomarker`	see `DualSimulations`
`...`	additional parameters from `Simulations`

Value

the DualSimulations object

Class for the simulations output from dual-endpoint model based designs

Description

This class captures the trial simulations from dual-endpoint model based designs. In comparison to the parent class Simulations, it contains additional slots to capture the dose-biomarker fits, and the sigma2W and rho estimates.

Slots

rhoEst: the vector of final posterior median rho estimates
sigma2West: the vector of final posterior median sigma2W estimates
fitBiomarker: list with the final dose-biomarker curve fits

Class for the summary of dual-endpoint simulations output

Description

In addition to the slots in the parent class SimulationsSummary, it contains two slots for the biomarker model fit information.

Details

Note that objects should not be created by users, therefore no initialization function is provided for this class.

Slots

biomarkerFitAtDoseMostSelected: fitted biomarker level at dose most often selected
meanBiomarkerFit: list with the average, lower (2.5 quantiles of the mean fitted biomarker level at each dose level

Initialization function for the "EffFlexi" class

Description

Initialization function for the "EffFlexi" class

Usage

EffFlexi(Eff, Effdose, sigma2, sigma2betaW, smooth = c("RW1", "RW2"), data)
EffFlexi(Eff, Effdose, sigma2, sigma2betaW, smooth = c("RW1", "RW2"), data)

Arguments

`Eff`	the pseudo efficacy responses
`Effdose`	the corresponding dose levels for the pseudo efficacy responses
`sigma2`	the prior variance of the efficacy responses which can be specified with a single positive scalar or with two positive scalar values fot the shape `a` and the rate `b` parameters of the inverse gamma distribtuion.
`sigma2betaW`	the prior variance of the random walk model used for smoothing which can be specified with a single postive scalar or with two positive scalars representing the shape `a` and the rate `b` parameter of the inverse gamma distribution.
`smooth`	used for smoothing data for this efficacy model. That is either the "RW1", the first-order random walk model or "RW2", the second-order random walk model is used of the mean efficacy responses.
`data`	the input data to update estimates of model parameters and follow the `DataDual` object class specification

Value

the EffFlexi class object

Class for the efficacy model in flexible form for prior expressed in form of pseudo data

Description

This is a class where a flexible form is used to describe the realtionship between the efficacy responses and the dose levels. This flexible form aims to capture different shape for the dose-efficacy curve and the mean efficacy responses at each dose level are estimated using MCMC. In addition, the first (RW1) or second order (RW2) random walk model can be used for smoothing data. That is the random walk model is used to model the first or the second order differnece of the mean efficacy responses to its neighbouring dose levels of their mean efficacy responses. The flexible form is specified as

$\mathbf{W}\vert\boldsymbol{\beta_w}, \sigma^2 \sim Normal (\mathbf{X}_w \boldsymbol{\beta_w}, \sigma^2 \mathbf{I})$

where $\mathbf{W}$ represent the column vector of the efficacy responses, $\boldsymbol{\beta_w}$ is th column vector of the mean efficacy responses for all dose levels, $\mathbf{X_w}$ is the design matrix with entries $I_{i(j)}$ which gives a value 1 if subject i is allocated to dose j. The $\sigma^2$ (sigma2) is the variance of the efficacy responses which can be either fixed or from an inverse gamma distribution.

Details

The RW1 model is given as

$\beta_{W,(j)} - \beta_{W,(j-1)} \sim Normal(0, \sigma^{2}_{\beta_{W}})$

where $\beta_{W,(j)}$ is the mean efficacy responses at dose j For the RW2 is given as

$\beta_{W,(j-2)} - 2 \beta_{W,(j-1)} + \beta_{W,(j)} \sim Normal(0, \sigma^{2}_{\beta_{W}})$

The variance parameter $\sigma^{2}_{\beta_{W}}$ . The variance $\sigma^{2}_{\beta_{W}}$ (sigma2betaW) will be the same at all dose levels and can either be fixed or assigned an inverse gamma prior distribution.

The Eff and Effdose are the pseduo efficacy responses and dose levels at which these pseudo efficacy responses are observed at. (see more details for Effloglog class) Eff and Effdose must be vector of at least length 2. The values or elements in vectors Eff or Effdose must put in the same position with its corresponding value in the other vector. The sigma2 is the prior variance of the flexible efficacy form. The variance is either specified with a single scalar value (fixed) or postive scalar value have to be specified for the a shape and b slope parameter for th inverse gamme distribtuion. Similarly, sigma2betaW is the prior variance of the random walk model which can be specified with a single scalar (fixed) value or specifying positive scalar values for the shape a and rate b parameters for the inverse gamma distributions. This model will output the updated value or the updated values of the paramters of the inverse gamma distributions for $sigma^2$ (sigma2) and $\sigma^2_{\beta_W}$ (sigma2betaW)

Slots

Eff: the pseudo efficacy responses. A vector of at least length 2 with the elements here and its corresponding value in Effdose must be specified in the same position. (see dtails above)
Effdose: the dose levels at which the pseudo efficacy responses are observed. This is a vector of at least length 2 and the elements here and its corrresponding value in Eff must be specified in the same postion. (see details from above)
sigma2: the prior variance of the flexible efficacy form. It can be specified with a single positive scalar or specifying a, the shape and b, the rate parameter of the inverse gamma distribution. (see details from above)
sigma2betaW: the prior variance of the random walk model for the mean efficact responses. A single positve scalar can be specified or specifying a, the shape and b, the rate parameter of the inverse gamma distribution (see details from above)
useFixed: a list of with logical value to each of the parameters sigma2 and sigma2betaw indicating whether a fixed value is used or not; this slot is needed for internal purposes and not to be touched by the user.
useRW1: for specifying the random walk model for the mean efficacy responses; if TRUE, first order random walk model is used, otherwise the second-order random walk model.
designW: is the design matrix for the efficacy responses. If only the pseudo efficacy responses are used, this will be the design matrix of the pseudo efficacy responses. If there are some observed efficacy responses available. It will be the design matrix based on both the pseudo and the observed efficacy responses.
RWmat: is the the difference matrix for the random walk model. This slot is needed for internal purposes and not to be touched by the user.
RWmatRank: is the rank of the difference matrix. This slot is needed for internal purposes and not to be touched by the user.

Examples

##Obtain prior estimates for the EffFlexi (efficacy model) given the pseudo data.
##First define an empty data set by only define the dose levels used in the study
## 12 dose levels are usesd from 25 to 300 mg with increments of 25.
emptydata<-DataDual(doseGrid=seq(25,300,25))
data<-emptydata
## define the pseudo data as first fixed 2 dose levels 25 and 300 mg and 
## specified in (Effdose slot).
## Then the efficacy responses observed at these two dose levels are 1.223 and 2.513 and 
## specified in (Eff slot).
## The prior variance of the pseudo efficay responses. This can be either a fixed value of 
## specifying the shape (a) and the rate (b) parameters for the inverse gamma distribution 
## in (sigma2 slot). The prior variance of the random walk model which can be a fixed value or 
## two value for the shape (a) and rate (b) parameter of the inverse gamma distribution in 
## (sigma2betaW slot). The data are specified in (data slot)


Effmodel<- EffFlexi(Eff=c(1.223, 2.513),Effdose=c(25,300),
                    sigma2=c(a=0.1,b=0.1),sigma2betaW=c(a=20,b=50),
                    smooth="RW2",data=data)

##Obtain estimates from the model given some observed responses
## first specified the data
data<-DataDual(x=c(25,50,50,75,100,100,225,300),y=c(0,0,0,0,1,1,1,1),
               w=c(0.31,0.42,0.59,0.45,0.6,0.7,0.6,0.52),
               doseGrid=seq(25,300,25))

Effmodel<- EffFlexi(Eff=c(1.223, 2.513),Effdose=c(25,300),
                    sigma2=c(a=0.1,b=0.1),sigma2betaW=c(a=20,b=50),
                    smooth="RW2",data=data)

##Obtain prior estimates for the EffFlexi (efficacy model) given the pseudo data.
##First define an empty data set by only define the dose levels used in the study
## 12 dose levels are usesd from 25 to 300 mg with increments of 25.
emptydata<-DataDual(doseGrid=seq(25,300,25))
data<-emptydata
## define the pseudo data as first fixed 2 dose levels 25 and 300 mg and 
## specified in (Effdose slot).
## Then the efficacy responses observed at these two dose levels are 1.223 and 2.513 and 
## specified in (Eff slot).
## The prior variance of the pseudo efficay responses. This can be either a fixed value of 
## specifying the shape (a) and the rate (b) parameters for the inverse gamma distribution 
## in (sigma2 slot). The prior variance of the random walk model which can be a fixed value or 
## two value for the shape (a) and rate (b) parameter of the inverse gamma distribution in 
## (sigma2betaW slot). The data are specified in (data slot)


Effmodel<- EffFlexi(Eff=c(1.223, 2.513),Effdose=c(25,300),
                    sigma2=c(a=0.1,b=0.1),sigma2betaW=c(a=20,b=50),
                    smooth="RW2",data=data)

##Obtain estimates from the model given some observed responses
## first specified the data
data<-DataDual(x=c(25,50,50,75,100,100,225,300),y=c(0,0,0,0,1,1,1,1),
               w=c(0.31,0.42,0.59,0.45,0.6,0.7,0.6,0.52),
               doseGrid=seq(25,300,25))

Effmodel<- EffFlexi(Eff=c(1.223, 2.513),Effdose=c(25,300),
                    sigma2=c(a=0.1,b=0.1),sigma2betaW=c(a=20,b=50),
                    smooth="RW2",data=data)

Initialization function for the "Effloglog" class

Description

Initialization function for the "Effloglog" class

Usage

Effloglog(Eff, Effdose, nu, data, c = 0)
Effloglog(Eff, Effdose, nu, data, c = 0)

Arguments

`Eff`	the pseudo efficacy responses
`Effdose`	the corresponding dose levels for the pseudo efficacy responses
`nu`	the precision (inverse of the variance) of the efficacy responses
`data`	the input data of `DataDual` class to update model estimates
`c`	the constant value added to the dose level when the dose level value is less than or equal to 1 and a special form of the linear log-log has to applied (Yeung et al. (2015).).

Value

the Effloglog object

Class for the linear log-log efficacy model using pseudo data prior

Description

This is the efficacy model which describe the relationship of the continuous efficacy responses and the dose levels. More specifically, this is a model to describe the linear relationship between the continuous efficacy responses and its coressponding dose level in log-log scale. The efficacy log-log model is given as

$y_i=\theta_1 +theta_2 log(log(d_i))+\epsilon_i$

where $y_i$ is the efficacy responses for subject i, $d_i$ is the dose level treated for subject i and $\epsilon_i$ is the random error term of efficacy model at subject i such that $\epsilon_i$ has a normal distribution of mean 0 and variance $\sigma^2=\nu^{-1}$ . This variance is assumed to be the same for all subjects.

Details

There are three parameters in this model which is to intercept $\theta_1$ , the slope $\theta_2$ and the precision $\nu$ of the efficay responses. It inherit all slots from ModelEff

The prior of this model is specified in form of pseudo data. First at least two dose levels are fixed. Then ask for experts' opinion about the efficacy values that can be obtained at each of the dose levels if one subject is treated at each of these dose levels. The prior modal estimates (same as the maximum likelihood estimates) can be obtained for the intercept and slope paramters in this model.

The Eff and Effdose are used to represent the prior in form of the pseudo data. The Eff represents the pseudo scalar efficacy values. The Effdose represents the dose levels at which these pseudo efficacy values are observed. These pseudo efficay values are always specified by assuming one subject are treated in each of the dose levels. Since at least 2 pseudo efficacy values are needed to obtain modal estimates of the intercept and slope parameters, both Eff and Effdose must be vector of at least length 2. The position of the values or elements specified in Eff or Effdose must be corresponds to the same elements or values in the other vector.

The nu represents the prior presion $\nu$ of the pseudo efficacy responses. It is also known as the inverse of the variance of the pseduo efficacy responses. The precision can be a fixed constant or having a gamma distribution. Therefore, single scalar value, a fixed value of the precision can be specified. If not, two positive scalar values must be specified as the shape and rate parameter of the gamma distribution. If there are some observed efficacy responses available, in the output, nu will display the updated value of the precision or the updated values for the parameters of the gamma distribution.

Given the variance of the pseudo efficacy responses, the joint prior distribution of the intercept $\theta_1$ (theta1) and the slope $\theta_2$ (theta2) of this model is a bivariate normal distribution. A conjugate posterior joint distribution is also used for theta1 and theta2. The joint prior bivariate normal distribution has mean $\boldsymbol\mu_0$ and covariance matrix $(\nu \mathbf{Q}_0)^{-1}$ . $\boldsymbol\mu_0$ is a $2 \times 1$ column vector contains the prior modal estimates of the intercept (theta1) and the slope (theta2). Based on $r$ for $r \geq 2$ pseudo efficacy responses specified, $\mathbf{X}_0$ will be the $r \times 2$ design matrix obtained for these pseudo efficacy responses. the matrix $\mathbf{Q}_0$ will be calculated by $\mathbf{Q}_0=\mathbf{X}_0 \mathbf{X}^T_0$ where $\nu$ is the precision of the pseudo efficacy responses. For the joint posterior bivariate distribution, we have $\boldsymbol{\mu}$ as the mean and $(\nu\mathbf{Q}_0)^{-1}$ as the covariance matrix. Here, $\boldsymbol\mu$ is the column vector containing the posterior modal estimates of the intercept (theta1) and the slope (theta2). The design matrix $\mathbf{X}$ obtained based only on observed efficacy responses will give $\mathbf{Q}=\mathbf{X}\mathbf{X}^T$ with $\nu$ as the precision of the observed efficay responses. If no observed efficay responses are availble (i.e only pseudo efficay responses are used), the vecmu, matX, matQ and vecY represents $\boldsymbol\mu_0$ , $\mathbf{X}_0$ , $\mathbf{Q}_0$ and the column vector of pseudo efficay responses, respectively. If there are some observed efficacy responses, vecmu, matX, matQ and vecY will represent $\boldsymbol\mu$ , $\mathbf{X}$ , $\mathbf{Q}$ and the column vector contains all observed efficacy responses, respectively. (see details in about the form of prior and posterior distribution)

Slots

Eff: the pseudo efficacy response, the scalar efficacy values. This must be a vector of at least length 2. Each element or value here must represents responses treated based on one subject. The order of its elements must corresponds to the values presented in vector Effdose (see details above)
Effdose: the pseudo efficacy dose level. This is the dose levels at which the pseudo efficacy responses are observed at. This must be a vector of at least length 2 and the orde of its elements must corresponds to values presented in vector Eff (see detial above)
nu: refers to the prior precision of pseudo efficacy responses. This is either a fixed value or a vector of elements a, a positive scalar for the shape parameter, and b, a positive scalar for the rate parameter for the gamma dsitribution. (see detail from above)
useFixed: a logical value if nu specified is a fixed value or not. This slot is needed for internal purposes and not to be touched by the user.
theta1: The intercept $\theta_1$ parameter of this efficacy log-log model. This slot is used in output to display the resulting prior or posterior modal estimates obtained based on the pseudo data and (if any) the observed data/ responses.
theta2: The slope $theta_2$ parameter of the efficacy log-lgo model. This slot is used in output to display the resulting prior or posterior modal estimates obtained based on the pseudo data and (if any) the observed data/ responses.
Pcov: refers to the covariance matrix of the intercept (phi1) and slope (phi2) paramters of this model. This slot is used in output to display the covariance matrix obtained based on the pseudo data and (if any) the observed data/responses. This slot is needed for internal purposes.
vecmu: is the column vector of the prior or the posterior modal estimates of the intercept (phi1) and the slope (phi2). This slot is used in output to display as the mean of the prior or posterior bivariate normal distribtuion for phi1 and phi2. (see details from above)
matX: is the design matrix based on either the pseudo or all observed efficacy response. This is used in output to display the design matrix for the pseudo or the observed efficacy responses (see details from above)
matQ: is the square matrix of multiplying the the design matrix with its transponse. This is represented either using the only the pseudo efficay responses or only with the observed efficacy responses. This is display in the output (see details from above)
vecY: is the column vector either contains the pseudo efficay responses or all the observed efficacy responses. This is used in output to display the pseudo or observed efficacy responses (see detail from above)
c: is a constant value greater or equal to 0, with the default 0 leading to the model form described above. In general, the model has the form $y_i=\theta_1 +theta_2 log(log(d_i + c))+\epsilon_i$ , such that dose levels greater than $1-c$ can be considered as described in Yeung et al. (2015).

Examples

##Obtain prior modal estimates for the Effloglog model (efficacy model) given the pseudo data.
##First define an empty data set by only define the dose levels used in the study,
## 12 dose levels are usesd from 25 to 300 mg with increments of 25.
emptydata<-DataDual(doseGrid=seq(25,300,25),placebo=FALSE)
data<-emptydata
## define the pseudo data as first fixed 2 dose levels 25 and 300 mg and specified in 
## (Effdose slot).
## Then the efficacy responses observed at these two dose levels are 1.223 and 2.513 and 
## specified in (Eff slot).
## The prior precision of the pseudo efficay responses. This can be either a fixed value of 
## specifying the shape (a) and the rate (b) parameters for the gamma distribution in (nu slot).
## Then specify all data currentl available in (data slot).

Effmodel<-Effloglog(Eff=c(1.223,2.513),Effdose=c(25,300),nu=c(a=1,b=0.025),data=data,c=0)

##Obtain posterior modal estimates and other estimates from the model given some observed responses
## If there is some observations available
## first specified the data
data<-DataDual(x=c(25,50,50,75,100,100,225,300),y=c(0,0,0,0,1,1,1,1),
               w=c(0.31,0.42,0.59,0.45,0.6,0.7,0.6,0.52),
               doseGrid=seq(25,300,25))

Effmodel<-Effloglog(Eff=c(1.223,2.513),Effdose=c(25,300),nu=c(a=1,b=0.025),data=data)


##Obtain prior modal estimates for the Effloglog model (efficacy model) given the pseudo data.
##First define an empty data set by only define the dose levels used in the study,
## 12 dose levels are usesd from 25 to 300 mg with increments of 25.
emptydata<-DataDual(doseGrid=seq(25,300,25),placebo=FALSE)
data<-emptydata
## define the pseudo data as first fixed 2 dose levels 25 and 300 mg and specified in 
## (Effdose slot).
## Then the efficacy responses observed at these two dose levels are 1.223 and 2.513 and 
## specified in (Eff slot).
## The prior precision of the pseudo efficay responses. This can be either a fixed value of 
## specifying the shape (a) and the rate (b) parameters for the gamma distribution in (nu slot).
## Then specify all data currentl available in (data slot).

Effmodel<-Effloglog(Eff=c(1.223,2.513),Effdose=c(25,300),nu=c(a=1,b=0.025),data=data,c=0)

##Obtain posterior modal estimates and other estimates from the model given some observed responses
## If there is some observations available
## first specified the data
data<-DataDual(x=c(25,50,50,75,100,100,225,300),y=c(0,0,0,0,1,1,1,1),
               w=c(0.31,0.42,0.59,0.45,0.6,0.7,0.6,0.52),
               doseGrid=seq(25,300,25))

Effmodel<-Effloglog(Eff=c(1.223,2.513),Effdose=c(25,300),nu=c(a=1,b=0.025),data=data)

Obtain hypothetical trial course table for a design

Description

This generic function takes a design and generates a dataframe showing the beginning of several hypothetical trial courses under the design. This means, from the generated dataframe one can read off: - how many cohorts are required in the optimal case (no DLTs observed) in order to reach the highest dose of the specified dose grid (or until the stopping rule is fulfilled) - assuming no DLTs are observed until a certain dose level, what the next recommended dose is for all possible number of DLTs observed - the actual relative increments that will be used in these cases - whether the trial would stop at a certain cohort Examining the "single trial" behavior of a dose escalation design is the first important step in evaluating a design, and cannot be replaced by studying solely the operating characteristics in "many trials". The cohort sizes are also taken from the design, assuming no DLTs occur until the dose listed.

Usage

examine(object, ..., maxNoIncrement = 100L)

## S4 method for signature 'Design'
examine(object, mcmcOptions = McmcOptions(), ..., maxNoIncrement)

## S4 method for signature 'RuleDesign'
examine(object, ..., maxNoIncrement = 100L)
examine(object, ..., maxNoIncrement = 100L)

## S4 method for signature 'Design'
examine(object, mcmcOptions = McmcOptions(), ..., maxNoIncrement)

## S4 method for signature 'RuleDesign'
examine(object, ..., maxNoIncrement = 100L)

Arguments

`object`	the design (`Design` or `RuleDesign` object) we want to examine
`...`	additional arguments (see methods)
`maxNoIncrement`	maximum number of contiguous next doses at 0 DLTs that are the same as before, i.e. no increment (default to 100)
`mcmcOptions`	object of class `McmcOptions`, giving the MCMC options for each evaluation in the trial. By default, the standard options are used

Value

The data frame

Functions

examine(Design): Examine a model-based CRM
examine(RuleDesign): Examine a rule-based design

Examples


# Define the dose-grid
emptydata <- Data(doseGrid = c(1, 3, 5, 10, 15, 20, 25))

# Initialize the CRM model 
model <- LogisticLogNormal(mean=c(-0.85, 1),
                           cov=
                             matrix(c(1, -0.5, -0.5, 1),
                                    nrow=2),
                           refDose=56)

# Choose the rule for selecting the next dose 
myNextBest <- NextBestNCRM(target=c(0.2, 0.35),
                           overdose=c(0.35, 1),
                           maxOverdoseProb=0.25)

# Choose the rule for the cohort-size 
mySize1 <- CohortSizeRange(intervals=c(0, 30),
                           cohortSize=c(1, 3))
mySize2 <- CohortSizeDLT(DLTintervals=c(0, 1),
                         cohortSize=c(1, 3))
mySize <- maxSize(mySize1, mySize2)

# Choose the rule for stopping
myStopping1 <- StoppingMinCohorts(nCohorts=3)
myStopping2 <- StoppingTargetProb(target=c(0.2, 0.35),
                                  prob=0.5)
myStopping3 <- StoppingMinPatients(nPatients=20)
myStopping <- (myStopping1 & myStopping2) | myStopping3

# Choose the rule for dose increments
myIncrements <- IncrementsRelative(intervals=c(0, 20),
                                   increments=c(1, 0.33))

# Initialize the design
design <- Design(model=model,
                 nextBest=myNextBest,
                 stopping=myStopping,
                 increments=myIncrements,
                 cohortSize=mySize,
                 data=emptydata,
                 startingDose=3)

# Examine the design
set.seed(4235)
# MCMC parameters are set to small values only to show this example. They should be
# increased for a real case.
options <- McmcOptions(burnin=10,step=1,samples=20)
examine(design, options)
  
## example where examine stops because stopping rule already fulfilled
myStopping4 <- StoppingMinPatients(nPatients=3)
myStopping <- (myStopping1 & myStopping2) | myStopping4
design <- Design(model=model,
                 nextBest=myNextBest,
                 stopping=myStopping,
                 increments=myIncrements,
                 cohortSize=mySize,
                 data=emptydata,
                 startingDose=3)
examine(design,mcmcOptions=options)

## example where examine stops because infinite looping
## (note that here a very low threshold is used for the parameter
## "maxNoIncrement" in "examine" to keep the execution time short)
myIncrements <- IncrementsRelative(intervals=c(0, 20),
                                   increments=c(1, 0.00001))
myStopping <- (myStopping1 & myStopping2) 
design <- Design(model=model,
                 nextBest=myNextBest,
                 stopping=myStopping,
                 increments=myIncrements,
                 cohortSize=mySize,
                 data=emptydata,
                 startingDose=3)
examine(design, mcmcOptions=options, maxNoIncrement = 2)

# Define the dose-grid
emptydata <- Data(doseGrid = c(5, 10, 15, 25, 35, 50, 80))

# inizialing a 3+3 design with constant cohort size of 3 and
# starting dose equal 5
myDesign <- RuleDesign(nextBest = NextBestThreePlusThree(),
                       cohortSize = CohortSizeConst(size=3L),
                       data = emptydata,
                       startingDose = 5)
  
# Examine the design
set.seed(4235)
examine(myDesign)
# Define the dose-grid
emptydata <- Data(doseGrid = c(1, 3, 5, 10, 15, 20, 25))

# Initialize the CRM model 
model <- LogisticLogNormal(mean=c(-0.85, 1),
                           cov=
                             matrix(c(1, -0.5, -0.5, 1),
                                    nrow=2),
                           refDose=56)

# Choose the rule for selecting the next dose 
myNextBest <- NextBestNCRM(target=c(0.2, 0.35),
                           overdose=c(0.35, 1),
                           maxOverdoseProb=0.25)

# Choose the rule for the cohort-size 
mySize1 <- CohortSizeRange(intervals=c(0, 30),
                           cohortSize=c(1, 3))
mySize2 <- CohortSizeDLT(DLTintervals=c(0, 1),
                         cohortSize=c(1, 3))
mySize <- maxSize(mySize1, mySize2)

# Choose the rule for stopping
myStopping1 <- StoppingMinCohorts(nCohorts=3)
myStopping2 <- StoppingTargetProb(target=c(0.2, 0.35),
                                  prob=0.5)
myStopping3 <- StoppingMinPatients(nPatients=20)
myStopping <- (myStopping1 & myStopping2) | myStopping3

# Choose the rule for dose increments
myIncrements <- IncrementsRelative(intervals=c(0, 20),
                                   increments=c(1, 0.33))

# Initialize the design
design <- Design(model=model,
                 nextBest=myNextBest,
                 stopping=myStopping,
                 increments=myIncrements,
                 cohortSize=mySize,
                 data=emptydata,
                 startingDose=3)

# Examine the design
set.seed(4235)
# MCMC parameters are set to small values only to show this example. They should be
# increased for a real case.
options <- McmcOptions(burnin=10,step=1,samples=20)
examine(design, options)
  
## example where examine stops because stopping rule already fulfilled
myStopping4 <- StoppingMinPatients(nPatients=3)
myStopping <- (myStopping1 & myStopping2) | myStopping4
design <- Design(model=model,
                 nextBest=myNextBest,
                 stopping=myStopping,
                 increments=myIncrements,
                 cohortSize=mySize,
                 data=emptydata,
                 startingDose=3)
examine(design,mcmcOptions=options)

## example where examine stops because infinite looping
## (note that here a very low threshold is used for the parameter
## "maxNoIncrement" in "examine" to keep the execution time short)
myIncrements <- IncrementsRelative(intervals=c(0, 20),
                                   increments=c(1, 0.00001))
myStopping <- (myStopping1 & myStopping2) 
design <- Design(model=model,
                 nextBest=myNextBest,
                 stopping=myStopping,
                 increments=myIncrements,
                 cohortSize=mySize,
                 data=emptydata,
                 startingDose=3)
examine(design, mcmcOptions=options, maxNoIncrement = 2)

# Define the dose-grid
emptydata <- Data(doseGrid = c(5, 10, 15, 25, 35, 50, 80))

# inizialing a 3+3 design with constant cohort size of 3 and
# starting dose equal 5
myDesign <- RuleDesign(nextBest = NextBestThreePlusThree(),
                       cohortSize = CohortSizeConst(size=3L),
                       data = emptydata,
                       startingDose = 5)
  
# Examine the design
set.seed(4235)
examine(myDesign)

Compute the expected efficacy based on a given dose, a given pseudo Efficacy log-log model and a given efficacy sample

Description

Compute the expected efficacy based on a given dose, a given pseudo Efficacy log-log model and a given efficacy sample

Usage

ExpEff(dose, model, samples, ...)

## S4 method for signature 'numeric,Effloglog,Samples'
ExpEff(dose, model, samples, ...)

## S4 method for signature 'numeric,Effloglog,missing'
ExpEff(dose, model, samples, ...)

## S4 method for signature 'numeric,EffFlexi,Samples'
ExpEff(dose, model, samples, ...)
ExpEff(dose, model, samples, ...)

## S4 method for signature 'numeric,Effloglog,Samples'
ExpEff(dose, model, samples, ...)

## S4 method for signature 'numeric,Effloglog,missing'
ExpEff(dose, model, samples, ...)

## S4 method for signature 'numeric,EffFlexi,Samples'
ExpEff(dose, model, samples, ...)

Arguments

`dose`	the dose
`model`	the `Effloglog` class object
`samples`	the `Samples` class object (can also be missing)
`...`	unused

Functions

ExpEff(dose = numeric, model = Effloglog, samples = Samples): Method for the Effloglog class
ExpEff(dose = numeric, model = Effloglog, samples = missing): Compute the Expected Efficacy based a given dose and a given Pseudo Efficacy log log model without samples
ExpEff(dose = numeric, model = EffFlexi, samples = Samples): Compute the Expected Efficacy based a given dose, Efficacy Flexible model with samples

Examples

##Obtain the expected efficacy value for a given dose, a given pseudo 
## efficacy model and a given efficacy sample
##The efficacy model must be from 'ModelEff' class (model slot)
##The efficacy sample must be from 'Samples' class (sample slot)
emptydata<-DataDual(doseGrid=seq(25,300,25))
data<-emptydata
model<- EffFlexi(Eff=c(1.223, 2.513),Effdose=c(25,300),
                 sigma2=c(a=0.1,b=0.1),sigma2betaW=c(a=20,b=50),smooth="RW2",data=data)
options<-McmcOptions(burnin=100,step=2,samples=200)
set.seed(94)
samples<-mcmc(data=data,model=model,options=options)
## Given the dose 75 (dose slot)
ExpEff(dose=75,model=model,samples=samples)
##Obtain the expected efficacy value for a given dose and a given pseudo efficacy model 

##The efficacy model must be from 'ModelEff' class (model slot)
emptydata<-DataDual(doseGrid=seq(25,300,25),placebo=FALSE)
data<-emptydata

model<-Effloglog(Eff=c(1.223,2.513),Effdose=c(25,300),nu=c(a=1,b=0.025),data=data,c=0)


## Given the dose 75 (dose slot)
ExpEff(dose=75,model=model)
##Obtain the expected efficacy value for a given dose, the 'EffFlexi' efficacy model and 
##samples generated from this efficacy model
##The efficacy model must be from 'EffFlexi' class (model slot)
##The efficacy samples must be from 'Samples' class (samples slot)
model<- EffFlexi(Eff=c(1.223, 2.513),Effdose=c(25,300),
                 sigma2=c(a=0.1,b=0.1),sigma2betaW=c(a=20,b=50),smooth="RW2",data=data)
set.seed(94)
samples<-mcmc(data=data,model=model,options=options)
## Given the dose 75 (dose slot)
ExpEff(dose=75,model=model,samples=samples)
##Obtain the expected efficacy value for a given dose, a given pseudo 
## efficacy model and a given efficacy sample
##The efficacy model must be from 'ModelEff' class (model slot)
##The efficacy sample must be from 'Samples' class (sample slot)
emptydata<-DataDual(doseGrid=seq(25,300,25))
data<-emptydata
model<- EffFlexi(Eff=c(1.223, 2.513),Effdose=c(25,300),
                 sigma2=c(a=0.1,b=0.1),sigma2betaW=c(a=20,b=50),smooth="RW2",data=data)
options<-McmcOptions(burnin=100,step=2,samples=200)
set.seed(94)
samples<-mcmc(data=data,model=model,options=options)
## Given the dose 75 (dose slot)
ExpEff(dose=75,model=model,samples=samples)
##Obtain the expected efficacy value for a given dose and a given pseudo efficacy model 

##The efficacy model must be from 'ModelEff' class (model slot)
emptydata<-DataDual(doseGrid=seq(25,300,25),placebo=FALSE)
data<-emptydata

model<-Effloglog(Eff=c(1.223,2.513),Effdose=c(25,300),nu=c(a=1,b=0.025),data=data,c=0)


## Given the dose 75 (dose slot)
ExpEff(dose=75,model=model)
##Obtain the expected efficacy value for a given dose, the 'EffFlexi' efficacy model and 
##samples generated from this efficacy model
##The efficacy model must be from 'EffFlexi' class (model slot)
##The efficacy samples must be from 'Samples' class (samples slot)
model<- EffFlexi(Eff=c(1.223, 2.513),Effdose=c(25,300),
                 sigma2=c(a=0.1,b=0.1),sigma2betaW=c(a=20,b=50),smooth="RW2",data=data)
set.seed(94)
samples<-mcmc(data=data,model=model,options=options)
## Given the dose 75 (dose slot)
ExpEff(dose=75,model=model,samples=samples)

Fit method for the Samples class

Description

Note this new generic function is necessary because the fitted function only allows the first argument object to appear in the signature. But we need also other arguments in the signature.

Usage

fit(object, model, data, ...)

## S4 method for signature 'Samples,Model,Data'
fit(
  object,
  model,
  data,
  points = data@doseGrid,
  quantiles = c(0.025, 0.975),
  middle = mean,
  ...
)

## S4 method for signature 'Samples,DualEndpoint,DataDual'
fit(object, model, data, quantiles = c(0.025, 0.975), middle = mean, ...)

## S4 method for signature 'Samples,LogisticIndepBeta,Data'
fit(
  object,
  model,
  data,
  points = data@doseGrid,
  quantiles = c(0.025, 0.975),
  middle = mean,
  ...
)

## S4 method for signature 'Samples,Effloglog,DataDual'
fit(
  object,
  model,
  data,
  points = data@doseGrid,
  quantiles = c(0.025, 0.975),
  middle = mean,
  ...
)

## S4 method for signature 'Samples,EffFlexi,DataDual'
fit(
  object,
  model,
  data,
  points = data@doseGrid,
  quantiles = c(0.025, 0.975),
  middle = mean,
  ...
)
fit(object, model, data, ...)

## S4 method for signature 'Samples,Model,Data'
fit(
  object,
  model,
  data,
  points = data@doseGrid,
  quantiles = c(0.025, 0.975),
  middle = mean,
  ...
)

## S4 method for signature 'Samples,DualEndpoint,DataDual'
fit(object, model, data, quantiles = c(0.025, 0.975), middle = mean, ...)

## S4 method for signature 'Samples,LogisticIndepBeta,Data'
fit(
  object,
  model,
  data,
  points = data@doseGrid,
  quantiles = c(0.025, 0.975),
  middle = mean,
  ...
)

## S4 method for signature 'Samples,Effloglog,DataDual'
fit(
  object,
  model,
  data,
  points = data@doseGrid,
  quantiles = c(0.025, 0.975),
  middle = mean,
  ...
)

## S4 method for signature 'Samples,EffFlexi,DataDual'
fit(
  object,
  model,
  data,
  points = data@doseGrid,
  quantiles = c(0.025, 0.975),
  middle = mean,
  ...
)

Arguments

`object`	the `Samples` object
`model`	the `Model` object
`data`	the `Data` object
`...`	unused
`points`	at which dose levels is the fit requested? default is the dose grid
`quantiles`	the quantiles to be calculated (default: 0.025 and 0.975)
`middle`	the function for computing the middle point. Default: `mean`

Value

the data frame with required information (see method details)

Functions

fit(object = Samples, model = Model, data = Data): This method returns a data frame with dose, middle, lower and upper quantiles for the dose-toxicity curve
fit(object = Samples, model = DualEndpoint, data = DataDual): This method returns a data frame with dose, and middle, lower and upper quantiles, for both the dose-tox and dose-biomarker (suffix "Biomarker") curves, for all grid points (Note that currently only the grid points can be used, because the DualEndpointRW models only allow that)
fit(object = Samples, model = LogisticIndepBeta, data = Data): This method return a data frame with dose, middle lower and upper quantiles for the dose-DLE curve using DLE samples for “LogisticIndepBeta” model class
fit(object = Samples, model = Effloglog, data = DataDual): This method returns a data frame with dose, middle, lower, upper quantiles for the dose-efficacy curve using efficacy samples for “Effloglog” model class
fit(object = Samples, model = EffFlexi, data = DataDual): This method returns a data frame with dose, middle, lower and upper quantiles for the dose-efficacy curve using efficacy samples for “EffFlexi” model class

Examples


# Create some data
data <- Data(x = c(0.1, 0.5, 1.5, 3, 6, 10, 10, 10),
             y = c(0, 0, 0, 0, 0, 0, 1, 0),
             cohort = c(0, 1, 2, 3, 4, 5, 5, 5),
             doseGrid = c(0.1, 0.5, 1.5, 3, 6,
                          seq(from = 10, to = 80, by=2)))

# Initialize a model 
model <- LogisticLogNormal(mean = c(-0.85, 1),
                           cov = matrix(c(1, -0.5, -0.5, 1), nrow = 2),
                           refDose = 56)

# Get posterior for all model parameters
options <- McmcOptions(burnin = 100,
                       step = 2,
                       samples = 2000)
set.seed(94)
samples <- mcmc(data, model, options)

# Extract the posterior mean  (and empirical 2.5 and 97.5 percentile)
# for the prob(DLT) by doses
fitted <- fit(object = samples,
              model = model,
              data = data,
              quantiles=c(0.025, 0.975),
              middle=mean)


# ----------------------------------------------
# A different example using a different model
## we need a data object with doses >= 1:
data<-Data(x=c(25,50,50,75,150,200,225,300),
           y=c(0,0,0,0,1,1,1,1),
           doseGrid=seq(from=25,to=300,by=25))


model <- LogisticIndepBeta(binDLE=c(1.05,1.8),
                           DLEweights=c(3,3),
                           DLEdose=c(25,300),
                           data=data)
options <- McmcOptions(burnin=100,
                       step=2,
                       samples=200)
## samples must be from 'Samples' class (object slot in fit)
samples <- mcmc(data,model,options)

fitted <- fit(object=samples, model=model, data=data)



# Create some data
data <- DataDual(
  x=c(0.1, 0.5, 1.5, 3, 6, 10, 10, 10,
      20, 20, 20, 40, 40, 40, 50, 50, 50),
  y=c(0, 0, 0, 0, 0, 0, 1, 0,
      0, 1, 1, 0, 0, 1, 0, 1, 1),
  w=c(0.31, 0.42, 0.59, 0.45, 0.6, 0.7, 0.55, 0.6,
      0.52, 0.54, 0.56, 0.43, 0.41, 0.39, 0.34, 0.38, 0.21),
  doseGrid=c(0.1, 0.5, 1.5, 3, 6,
             seq(from=10, to=80, by=2)))

# Initialize the Dual-Endpoint model (in this case RW1)
model <- DualEndpointRW(mu = c(0, 1),
                        Sigma = matrix(c(1, 0, 0, 1), nrow=2),
                        sigma2betaW = 0.01,
                        sigma2W = c(a=0.1, b=0.1),
                        rho = c(a=1, b=1),
                        smooth = "RW1")

# Set-up some MCMC parameters and generate samples from the posterior
options <- McmcOptions(burnin=100,
                       step=2,
                       samples=500)
set.seed(94)
samples <- mcmc(data, model, options)

# Extract the posterior mean  (and empirical 2.5 and 97.5 percentile)
# for the prob(DLT) by doses and the Biomarker by doses
fitted <- fit(object = samples,
              model = model,
              data = data,
              quantiles=c(0.025, 0.975),
              middle=mean)
##Obtain the 'fit' the middle, uppper and lower quantiles for the dose-DLE curve
## at all dose levels using a DLE sample, a DLE model and the data
## samples must be from 'Samples' class (object slot)
## we need a data object with doses >= 1:
data<-Data(x=c(25,50,50,75,150,200,225,300),
           y=c(0,0,0,0,1,1,1,1),
           doseGrid=seq(from=25,to=300,by=25))
## model must be from 'Model' or 'ModelTox' class e.g using 'LogisticIbdepBeta' model class
model<-LogisticIndepBeta(binDLE=c(1.05,1.8),DLEweights=c(3,3),DLEdose=c(25,300),data=data)
##options for MCMC
options<-McmcOptions(burnin=100,step=2,samples=200)
## samples must be from 'Samples' class (object slot in fit)
samples<-mcmc(data,model,options)

fit(object=samples, model=model,data=data)
##Obtain the 'fit' the middle, uppper and lower quantiles for the dose-efficacy curve
## at all dose levels using an efficacy sample, a pseudo efficacy model and the data
## data must be from 'DataDual' class
data<-DataDual(x=c(25,50,25,50,75,300,250,150),
               y=c(0,0,0,0,0,1,1,0),
               w=c(0.31,0.42,0.59,0.45,0.6,0.7,0.6,0.52),
               doseGrid=seq(25,300,25),
               placebo=FALSE)
## model must be from 'ModelEff' e.g using 'Effloglog' class
Effmodel<-Effloglog(c(1.223,2.513),c(25,300),nu=c(a=1,b=0.025),data=data,c=0)
## samples must be from 'Samples' class (object slot in fit)
options<-McmcOptions(burnin=100,step=2,samples=200)
Effsamples <- mcmc(data=data,model=Effmodel,options=options)
fit(object=Effsamples, model=Effmodel,data=data)
##Obtain the 'fit' the middle, uppper and lower quantiles for the dose-efficacy curve
## at all dose levels using an efficacy sample, the 'EffFlexi' efficacy model and the data
## data must be from 'DataDual' class
data<-DataDual(x=c(25,50,25,50,75,300,250,150),
               y=c(0,0,0,0,0,1,1,0),
               w=c(0.31,0.42,0.59,0.45,0.6,0.7,0.6,0.52),
               doseGrid=seq(25,300,25),
               placebo=FALSE)
## model must be from 'ModelEff' e.g using 'Effloglog' class
Effmodel<- EffFlexi(Eff=c(1.223, 2.513),Effdose=c(25,300),
                    sigma2=c(a=0.1,b=0.1),sigma2betaW=c(a=20,b=50),smooth="RW2",data=data)

## samples must be from 'Samples' class (object slot in fit)
options<-McmcOptions(burnin=100,step=2,samples=200)
Effsamples <- mcmc(data=data,model=Effmodel,options=options)
fit(object=Effsamples, model=Effmodel,data=data)
# Create some data
data <- Data(x = c(0.1, 0.5, 1.5, 3, 6, 10, 10, 10),
             y = c(0, 0, 0, 0, 0, 0, 1, 0),
             cohort = c(0, 1, 2, 3, 4, 5, 5, 5),
             doseGrid = c(0.1, 0.5, 1.5, 3, 6,
                          seq(from = 10, to = 80, by=2)))

# Initialize a model 
model <- LogisticLogNormal(mean = c(-0.85, 1),
                           cov = matrix(c(1, -0.5, -0.5, 1), nrow = 2),
                           refDose = 56)

# Get posterior for all model parameters
options <- McmcOptions(burnin = 100,
                       step = 2,
                       samples = 2000)
set.seed(94)
samples <- mcmc(data, model, options)

# Extract the posterior mean  (and empirical 2.5 and 97.5 percentile)
# for the prob(DLT) by doses
fitted <- fit(object = samples,
              model = model,
              data = data,
              quantiles=c(0.025, 0.975),
              middle=mean)


# ----------------------------------------------
# A different example using a different model
## we need a data object with doses >= 1:
data<-Data(x=c(25,50,50,75,150,200,225,300),
           y=c(0,0,0,0,1,1,1,1),
           doseGrid=seq(from=25,to=300,by=25))


model <- LogisticIndepBeta(binDLE=c(1.05,1.8),
                           DLEweights=c(3,3),
                           DLEdose=c(25,300),
                           data=data)
options <- McmcOptions(burnin=100,
                       step=2,
                       samples=200)
## samples must be from 'Samples' class (object slot in fit)
samples <- mcmc(data,model,options)

fitted <- fit(object=samples, model=model, data=data)



# Create some data
data <- DataDual(
  x=c(0.1, 0.5, 1.5, 3, 6, 10, 10, 10,
      20, 20, 20, 40, 40, 40, 50, 50, 50),
  y=c(0, 0, 0, 0, 0, 0, 1, 0,
      0, 1, 1, 0, 0, 1, 0, 1, 1),
  w=c(0.31, 0.42, 0.59, 0.45, 0.6, 0.7, 0.55, 0.6,
      0.52, 0.54, 0.56, 0.43, 0.41, 0.39, 0.34, 0.38, 0.21),
  doseGrid=c(0.1, 0.5, 1.5, 3, 6,
             seq(from=10, to=80, by=2)))

# Initialize the Dual-Endpoint model (in this case RW1)
model <- DualEndpointRW(mu = c(0, 1),
                        Sigma = matrix(c(1, 0, 0, 1), nrow=2),
                        sigma2betaW = 0.01,
                        sigma2W = c(a=0.1, b=0.1),
                        rho = c(a=1, b=1),
                        smooth = "RW1")

# Set-up some MCMC parameters and generate samples from the posterior
options <- McmcOptions(burnin=100,
                       step=2,
                       samples=500)
set.seed(94)
samples <- mcmc(data, model, options)

# Extract the posterior mean  (and empirical 2.5 and 97.5 percentile)
# for the prob(DLT) by doses and the Biomarker by doses
fitted <- fit(object = samples,
              model = model,
              data = data,
              quantiles=c(0.025, 0.975),
              middle=mean)
##Obtain the 'fit' the middle, uppper and lower quantiles for the dose-DLE curve
## at all dose levels using a DLE sample, a DLE model and the data
## samples must be from 'Samples' class (object slot)
## we need a data object with doses >= 1:
data<-Data(x=c(25,50,50,75,150,200,225,300),
           y=c(0,0,0,0,1,1,1,1),
           doseGrid=seq(from=25,to=300,by=25))
## model must be from 'Model' or 'ModelTox' class e.g using 'LogisticIbdepBeta' model class
model<-LogisticIndepBeta(binDLE=c(1.05,1.8),DLEweights=c(3,3),DLEdose=c(25,300),data=data)
##options for MCMC
options<-McmcOptions(burnin=100,step=2,samples=200)
## samples must be from 'Samples' class (object slot in fit)
samples<-mcmc(data,model,options)

fit(object=samples, model=model,data=data)
##Obtain the 'fit' the middle, uppper and lower quantiles for the dose-efficacy curve
## at all dose levels using an efficacy sample, a pseudo efficacy model and the data
## data must be from 'DataDual' class
data<-DataDual(x=c(25,50,25,50,75,300,250,150),
               y=c(0,0,0,0,0,1,1,0),
               w=c(0.31,0.42,0.59,0.45,0.6,0.7,0.6,0.52),
               doseGrid=seq(25,300,25),
               placebo=FALSE)
## model must be from 'ModelEff' e.g using 'Effloglog' class
Effmodel<-Effloglog(c(1.223,2.513),c(25,300),nu=c(a=1,b=0.025),data=data,c=0)
## samples must be from 'Samples' class (object slot in fit)
options<-McmcOptions(burnin=100,step=2,samples=200)
Effsamples <- mcmc(data=data,model=Effmodel,options=options)
fit(object=Effsamples, model=Effmodel,data=data)
##Obtain the 'fit' the middle, uppper and lower quantiles for the dose-efficacy curve
## at all dose levels using an efficacy sample, the 'EffFlexi' efficacy model and the data
## data must be from 'DataDual' class
data<-DataDual(x=c(25,50,25,50,75,300,250,150),
               y=c(0,0,0,0,0,1,1,0),
               w=c(0.31,0.42,0.59,0.45,0.6,0.7,0.6,0.52),
               doseGrid=seq(25,300,25),
               placebo=FALSE)
## model must be from 'ModelEff' e.g using 'Effloglog' class
Effmodel<- EffFlexi(Eff=c(1.223, 2.513),Effdose=c(25,300),
                    sigma2=c(a=0.1,b=0.1),sigma2betaW=c(a=20,b=50),smooth="RW2",data=data)

## samples must be from 'Samples' class (object slot in fit)
options<-McmcOptions(burnin=100,step=2,samples=200)
Effsamples <- mcmc(data=data,model=Effmodel,options=options)
fit(object=Effsamples, model=Effmodel,data=data)

Get the fiited values for the gain values at all dose levels based on a given pseudo DLE model, DLE sample, a pseudo efficacy model, a Efficacy sample and data. This method returns a data frame with dose, middle, lower and upper quantiles of the gain value samples

Description

Get the fiited values for the gain values at all dose levels based on a given pseudo DLE model, DLE sample, a pseudo efficacy model, a Efficacy sample and data. This method returns a data frame with dose, middle, lower and upper quantiles of the gain value samples

Usage

fitGain(DLEmodel, DLEsamples, Effmodel, Effsamples, data, ...)

## S4 method for signature 'ModelTox,Samples,ModelEff,Samples,DataDual'
fitGain(
  DLEmodel,
  DLEsamples,
  Effmodel,
  Effsamples,
  data,
  points = data@doseGrid,
  quantiles = c(0.025, 0.975),
  middle = mean,
  ...
)
fitGain(DLEmodel, DLEsamples, Effmodel, Effsamples, data, ...)

## S4 method for signature 'ModelTox,Samples,ModelEff,Samples,DataDual'
fitGain(
  DLEmodel,
  DLEsamples,
  Effmodel,
  Effsamples,
  data,
  points = data@doseGrid,
  quantiles = c(0.025, 0.975),
  middle = mean,
  ...
)

Arguments

`DLEmodel`	the DLE pseudo model of `ModelTox` class object
`DLEsamples`	the DLE samples of `Samples` class object
`Effmodel`	the efficacy pseudo model of `ModelEff` class object
`Effsamples`	the efficacy samples of `Samples` class object
`data`	the data input of `DataDual` class object
`...`	additional arguments for methods
`points`	at which dose levels is the fit requested? default is the dose grid
`quantiles`	the quantiles to be calculated (default: 0.025 and 0.975)
`middle`	the function for computing the middle point. Default: `mean`

Functions

fitGain( DLEmodel = ModelTox, DLEsamples = Samples, Effmodel = ModelEff, Effsamples = Samples, data = DataDual ): This method returns a data frame with dose, middle, lower, upper quantiles for the gain values obtained given the DLE and the efficacy samples

Examples

##Obtain the 'fitGain' the middle, uppper and lower quantiles for the samples of gain values
## at all dose levels using a pseudo DLE model, a DLE sample, a pseudo Efficacy model and
## a efficacy sample
## data must be from 'DataDual' class
data<-DataDual(x=c(25,50,25,50,75,300,250,150),
               y=c(0,0,0,0,0,1,1,0),
               w=c(0.31,0.42,0.59,0.45,0.6,0.7,0.6,0.52),
               doseGrid=seq(25,300,25),
               placebo=FALSE)
## DLE model must be from 'ModelTox' class e.g using 'LogisticIndepBeta' model
DLEmodel<-LogisticIndepBeta(binDLE=c(1.05,1.8),DLEweights=c(3,3),DLEdose=c(25,300),data=data)

## Efficacy model must be from 'ModelEff' class e.g using 'Effloglog' model
Effmodel<-Effloglog(c(1.223,2.513),c(25,300),nu=c(a=1,b=0.025),data=data,c=0)
## samples must be from 'Samples' class (object slot in fit)
options<-McmcOptions(burnin=100,step=2,samples=200)
##set up the same data set in class 'Data' for MCMC sampling for DLE
data1 <- Data(x=data@x,y=data@y,doseGrid=data@doseGrid)

DLEsamples <- mcmc(data=data1,model=DLEmodel,options=options)
Effsamples <- mcmc(data=data,model=Effmodel,options=options)

fitGain(DLEmodel=DLEmodel,DLEsamples=DLEsamples,
        Effmodel=Effmodel, Effsamples=Effsamples,data=data)
##Obtain the 'fitGain' the middle, uppper and lower quantiles for the samples of gain values
## at all dose levels using a pseudo DLE model, a DLE sample, a pseudo Efficacy model and
## a efficacy sample
## data must be from 'DataDual' class
data<-DataDual(x=c(25,50,25,50,75,300,250,150),
               y=c(0,0,0,0,0,1,1,0),
               w=c(0.31,0.42,0.59,0.45,0.6,0.7,0.6,0.52),
               doseGrid=seq(25,300,25),
               placebo=FALSE)
## DLE model must be from 'ModelTox' class e.g using 'LogisticIndepBeta' model
DLEmodel<-LogisticIndepBeta(binDLE=c(1.05,1.8),DLEweights=c(3,3),DLEdose=c(25,300),data=data)

## Efficacy model must be from 'ModelEff' class e.g using 'Effloglog' model
Effmodel<-Effloglog(c(1.223,2.513),c(25,300),nu=c(a=1,b=0.025),data=data,c=0)
## samples must be from 'Samples' class (object slot in fit)
options<-McmcOptions(burnin=100,step=2,samples=200)
##set up the same data set in class 'Data' for MCMC sampling for DLE
data1 <- Data(x=data@x,y=data@y,doseGrid=data@doseGrid)

DLEsamples <- mcmc(data=data1,model=DLEmodel,options=options)
Effsamples <- mcmc(data=data,model=Effmodel,options=options)

fitGain(DLEmodel=DLEmodel,DLEsamples=DLEsamples,
        Effmodel=Effmodel, Effsamples=Effsamples,data=data)

Compute the gain value with a given dose level, given a pseudo DLE model, a DLE sample, a pseudo Efficacy log-log model and a Efficacy sample

Description

Compute the gain value with a given dose level, given a pseudo DLE model, a DLE sample, a pseudo Efficacy log-log model and a Efficacy sample

Usage

gain(dose, DLEmodel, DLEsamples, Effmodel, Effsamples, ...)

## S4 method for signature 'numeric,ModelTox,Samples,Effloglog,Samples'
gain(dose, DLEmodel, DLEsamples, Effmodel, Effsamples, ...)

## S4 method for signature 'numeric,ModelTox,Samples,EffFlexi,Samples'
gain(dose, DLEmodel, DLEsamples, Effmodel, Effsamples, ...)

## S4 method for signature 'numeric,ModelTox,missing,Effloglog,missing'
gain(dose, DLEmodel, DLEsamples, Effmodel, Effsamples, ...)
gain(dose, DLEmodel, DLEsamples, Effmodel, Effsamples, ...)

## S4 method for signature 'numeric,ModelTox,Samples,Effloglog,Samples'
gain(dose, DLEmodel, DLEsamples, Effmodel, Effsamples, ...)

## S4 method for signature 'numeric,ModelTox,Samples,EffFlexi,Samples'
gain(dose, DLEmodel, DLEsamples, Effmodel, Effsamples, ...)

## S4 method for signature 'numeric,ModelTox,missing,Effloglog,missing'
gain(dose, DLEmodel, DLEsamples, Effmodel, Effsamples, ...)

Arguments

`dose`	the dose
`DLEmodel`	the `ModelTox` object
`DLEsamples`	the `Samples` object (can also be missing)
`Effmodel`	the `Effloglog` or the `EffFlexi` object
`Effsamples`	the `Samples` object (can also be missing)
`...`	unused

Functions

gain( dose = numeric, DLEmodel = ModelTox, DLEsamples = Samples, Effmodel = EffFlexi, Effsamples = Samples ): Compute the gain given a dose level, a pseduo DLE model, a DLE sample, the pseudo EffFlexi model and an Efficacy sample
gain( dose = numeric, DLEmodel = ModelTox, DLEsamples = missing, Effmodel = Effloglog, Effsamples = missing ): Compute the gain value given a dose level, a pseudo DLE model and a pseudo efficacy model of Effloglog class object without DLE and the efficacy sample

Examples

##Obtain the gain value for a given dose, a pseudo DLE model, a DLE sample, 
## a pseudo efficacy model and an efficacy sample
##The DLE model must be from 'ModelTox' class (DLEmodel slot)
emptydata<- DataDual(doseGrid=seq(25,300,25),placebo=FALSE)
data<-emptydata
DLEmodel<-LogisticIndepBeta(binDLE=c(1.05,1.8),DLEweights=c(3,3),DLEdose=c(25,300),data=data)
DLEsamples <- mcmc(data, DLEmodel, McmcOptions(burnin=100,step=2,samples=200))

##The efficacy model must be from 'ModelEff' class (Effmodel slot)
## The DLE and efficayc samples must be from 'Samples' class (DLEsamples and Effsamples slot)
Effmodel<-Effloglog(Eff=c(1.223,2.513),Effdose=c(25,300),nu=c(a=1,b=0.025),data=data,c=0)
Effsamples <- mcmc(data, Effmodel, McmcOptions(burnin=100,step=2,samples=200))

## Given a dose level 75,
gain(dose=75,DLEmodel=DLEmodel,DLEsamples=DLEsamples,Effmodel=Effmodel,Effsamples=Effsamples)
##Obtain the gain value for a given dose, a pseudo DLE model, a DLE sample, 
## the 'EffFlexi' efficacy model and an efficacy sample
##The DLE model must be from 'ModelTox' class (DLEmodel slot)
emptydata<- DataDual(doseGrid=seq(25,300,25))
data<-emptydata
DLEmodel<-LogisticIndepBeta(binDLE=c(1.05,1.8),DLEweights=c(3,3),DLEdose=c(25,300),data=data)
DLEsamples <- mcmc(data, DLEmodel, McmcOptions(burnin=100,step=2,samples=200))

##The efficacy model must be from 'EffFlexi' class (Effmodel slot)
## The DLE and efficayc samples must be from 'Samples' class (DLEsamples and Effsamples slot)
EffFleximodel <- EffFlexi(Eff=c(1.223, 2.513),Effdose=c(25,300),
                     sigma2=c(a=0.1,b=0.1),sigma2betaW=c(a=20,b=50),smooth="RW2",data=data)
Effsamples <- mcmc(data, EffFleximodel, McmcOptions(burnin=100,step=2,samples=200))

## Given a dose level 75,
gain(dose=75,DLEmodel=DLEmodel,DLEsamples=DLEsamples,Effmodel=EffFleximodel,Effsamples=Effsamples)
##Obtain the gain value for a given dose, a pseudo DLE model and  a pseudo efficacy model
## without samples
##The DLE model must be from 'ModelTox' class (DLEmodel slot)
emptydata<- DataDual(doseGrid=seq(25,300,25),placebo=FALSE)
data<-Data(doseGrid=seq(25,300,25),placebo=FALSE)

DLEmodel<-LogisticIndepBeta(binDLE=c(1.05,1.8),DLEweights=c(3,3),DLEdose=c(25,300),data=data)
##The efficacy model must be from 'Effloglog' class  (Effmodel slot)
Effmodel<-Effloglog(Eff=c(1.223,2.513),Effdose=c(25,300),nu=c(a=1,b=0.025),data=emptydata,c=0)
## Given a dose level 75,
gain(dose=75,DLEmodel=DLEmodel,Effmodel=Effmodel)
##Obtain the gain value for a given dose, a pseudo DLE model, a DLE sample, 
## a pseudo efficacy model and an efficacy sample
##The DLE model must be from 'ModelTox' class (DLEmodel slot)
emptydata<- DataDual(doseGrid=seq(25,300,25),placebo=FALSE)
data<-emptydata
DLEmodel<-LogisticIndepBeta(binDLE=c(1.05,1.8),DLEweights=c(3,3),DLEdose=c(25,300),data=data)
DLEsamples <- mcmc(data, DLEmodel, McmcOptions(burnin=100,step=2,samples=200))

##The efficacy model must be from 'ModelEff' class (Effmodel slot)
## The DLE and efficayc samples must be from 'Samples' class (DLEsamples and Effsamples slot)
Effmodel<-Effloglog(Eff=c(1.223,2.513),Effdose=c(25,300),nu=c(a=1,b=0.025),data=data,c=0)
Effsamples <- mcmc(data, Effmodel, McmcOptions(burnin=100,step=2,samples=200))

## Given a dose level 75,
gain(dose=75,DLEmodel=DLEmodel,DLEsamples=DLEsamples,Effmodel=Effmodel,Effsamples=Effsamples)
##Obtain the gain value for a given dose, a pseudo DLE model, a DLE sample, 
## the 'EffFlexi' efficacy model and an efficacy sample
##The DLE model must be from 'ModelTox' class (DLEmodel slot)
emptydata<- DataDual(doseGrid=seq(25,300,25))
data<-emptydata
DLEmodel<-LogisticIndepBeta(binDLE=c(1.05,1.8),DLEweights=c(3,3),DLEdose=c(25,300),data=data)
DLEsamples <- mcmc(data, DLEmodel, McmcOptions(burnin=100,step=2,samples=200))

##The efficacy model must be from 'EffFlexi' class (Effmodel slot)
## The DLE and efficayc samples must be from 'Samples' class (DLEsamples and Effsamples slot)
EffFleximodel <- EffFlexi(Eff=c(1.223, 2.513),Effdose=c(25,300),
                     sigma2=c(a=0.1,b=0.1),sigma2betaW=c(a=20,b=50),smooth="RW2",data=data)
Effsamples <- mcmc(data, EffFleximodel, McmcOptions(burnin=100,step=2,samples=200))

## Given a dose level 75,
gain(dose=75,DLEmodel=DLEmodel,DLEsamples=DLEsamples,Effmodel=EffFleximodel,Effsamples=Effsamples)
##Obtain the gain value for a given dose, a pseudo DLE model and  a pseudo efficacy model
## without samples
##The DLE model must be from 'ModelTox' class (DLEmodel slot)
emptydata<- DataDual(doseGrid=seq(25,300,25),placebo=FALSE)
data<-Data(doseGrid=seq(25,300,25),placebo=FALSE)

DLEmodel<-LogisticIndepBeta(binDLE=c(1.05,1.8),DLEweights=c(3,3),DLEdose=c(25,300),data=data)
##The efficacy model must be from 'Effloglog' class  (Effmodel slot)
Effmodel<-Effloglog(Eff=c(1.223,2.513),Effdose=c(25,300),nu=c(a=1,b=0.025),data=emptydata,c=0)
## Given a dose level 75,
gain(dose=75,DLEmodel=DLEmodel,Effmodel=Effmodel)

Class for general data input

Description

Class for general data input

Slots

ID: unique patient IDs (integer vector)
cohort: the cohort indices (sorted values from 0, 1, 2, ...)
nObs: number of observations

No Intitialization function for this General class for model input

Description

This is the general model class, from which all other specific models for involving BUGS (the software) for computing result. It inherits all slots from AllModels

Details

The datamodel must obey the convention that the data input is called exactly as in the corresponding data class. All prior distributions for parameters should be contained in the model function priormodel. The background is that this can be used to simulate from the prior distribution, before obtaining any data.

Slots

datamodel: a function representing the BUGS data model specification (see the details above)
priormodel: a function representing the BUGS prior specification (see the details above)
modelspecs: a function computing the list of the data model and prior model specifications that are required for fully specifying them (e.g. prior parameters, reference dose, etc.), based on the data slots that are then required as arguments of this function. This will then be passed to BUGS for the computations.
init: a function computing the list of starting values for parameters required to be initialized in the MCMC sampler, based on the data slots that are then required as arguments of this function
sample: names of all parameters from which you would like to save the MCMC samples.

Initialization function for "GeneralSimulations"

Description

Initialization function for "GeneralSimulations"

Usage

GeneralSimulations(data, doses, seed)
GeneralSimulations(data, doses, seed)

Arguments

`data`	see `GeneralSimulations`
`doses`	see `GeneralSimulations`
`seed`	see `GeneralSimulations`

Value

the GeneralSimulations object

General class for the simulations output

Description

This class captures trial simulations.

Details

Here also the random generator state before starting the simulation is saved, in order to be able to reproduce the outcome. For this just use set.seed with the seed as argument before running simulate,Design-method.

Slots

data: list of produced Data objects
doses: the vector of final dose recommendations
seed: random generator state before starting the simulation

Class for the summary of general simulations output

Description

Note that objects should not be created by users, therefore no initialization function is provided for this class.

Slots

target: target toxicity interval
targetDoseInterval: corresponding target dose interval
nsim: number of simulations
propDLTs: proportions of DLTs in the trials
meanToxRisk: mean toxicity risks for the patients
doseSelected: doses selected as MTD
toxAtDosesSelected: true toxicity at doses selected
propAtTarget: Proportion of trials selecting target MTD
doseMostSelected: dose most often selected as MTD
obsToxRateAtDoseMostSelected: observed toxicity rate at dose most often selected
nObs: number of patients overall
nAboveTarget: number of patients treated above target tox interval
doseGrid: the dose grid that has been used
placebo: set to TRUE (default is FALSE) for a design with placebo

Get specific parameter samples and produce a data.frame

Description

Here you have to specify with pos which parameter you would like to extract from the Samples object

Usage

## S4 method for signature 'Samples,character'
get(x, pos = -1L, envir = NULL, mode = NULL, inherits = NULL)
## S4 method for signature 'Samples,character'
get(x, pos = -1L, envir = NULL, mode = NULL, inherits = NULL)

Arguments

`x`	the `Samples` object
`pos`	the name of the parameter
`envir`	for vectorial parameters, you can give the indices of the elements you would like to extract. If `NULL`, the whole vector samples will be returned
`mode`	not used
`inherits`	not used

Value

the data frame suitable for use with ggmcmc

Examples


# Create some data
data <- Data(x = c(0.1, 0.5, 1.5, 3, 6, 10, 10, 10),
             y = c(0, 0, 0, 0, 0, 0, 1, 0),
             cohort = c(0, 1, 2, 3, 4, 5, 5, 5),
             doseGrid = c(0.1, 0.5, 1.5, 3, 6,
                          seq(from = 10, to = 80, by=2)))

# Initialize a model 
model <- LogisticLogNormal(mean = c(-0.85, 1),
                           cov = matrix(c(1, -0.5, -0.5, 1), nrow = 2),
                           refDose = 56)

# Get posterior for all model parameters
options <- McmcOptions(burnin = 100,
                       step = 2,
                       samples = 2000)
set.seed(94)
samples <- mcmc(data, model, options)

# now extract the alpha0 samples (intercept of the regression model)
alpha0samples <- get(samples, "alpha0")
# Create some data
data <- Data(x = c(0.1, 0.5, 1.5, 3, 6, 10, 10, 10),
             y = c(0, 0, 0, 0, 0, 0, 1, 0),
             cohort = c(0, 1, 2, 3, 4, 5, 5, 5),
             doseGrid = c(0.1, 0.5, 1.5, 3, 6,
                          seq(from = 10, to = 80, by=2)))

# Initialize a model 
model <- LogisticLogNormal(mean = c(-0.85, 1),
                           cov = matrix(c(1, -0.5, -0.5, 1), nrow = 2),
                           refDose = 56)

# Get posterior for all model parameters
options <- McmcOptions(burnin = 100,
                       step = 2,
                       samples = 2000)
set.seed(94)
samples <- mcmc(data, model, options)

# now extract the alpha0 samples (intercept of the regression model)
alpha0samples <- get(samples, "alpha0")

Extracting efficacy responses for subjects without or with a DLE. This is a class where we separate efficacy responses with or without a DLE. It outputs the efficacy responses and their corresponding dose levels treated at in two categories (with or without DLE)

Description

Extracting efficacy responses for subjects without or with a DLE. This is a class where we separate efficacy responses with or without a DLE. It outputs the efficacy responses and their corresponding dose levels treated at in two categories (with or without DLE)

Usage

getEff(object, ...)

## S4 method for signature 'DataDual'
getEff(object, x, y, w, ...)
getEff(object, ...)

## S4 method for signature 'DataDual'
getEff(object, x, y, w, ...)

Arguments

`object`	for data input from `DataDual` object
`...`	unused
`x`	todo
`y`	todo
`w`	todo

Examples

## Separate the efficacy responses of subjects with or without DLE
## data must be specified for in 'DataDual' class
data<-DataDual(x=c(25,50,25,50,75,300,250,150),
               y=c(0,0,0,0,0,1,1,0),
               w=c(0.31,0.42,0.59,0.45,0.6,0.7,0.6,0.52),
               doseGrid=seq(25,300,25))
##Display the efficacy response and their corresponding dose levels 
## treated at when no or a DLE is observed
getEff(data)
## Separate the efficacy responses of subjects with or without DLE
## data must be specified for in 'DataDual' class
data<-DataDual(x=c(25,50,25,50,75,300,250,150),
               y=c(0,0,0,0,0,1,1,0),
               w=c(0.31,0.42,0.59,0.45,0.6,0.7,0.6,0.52),
               doseGrid=seq(25,300,25))
##Display the efficacy response and their corresponding dose levels 
## treated at when no or a DLE is observed
getEff(data)

Get the minimal informative unimodal beta distribution

Description

As defined in Neuenschwander et al (2008), this function computes the parameters of the minimal informative unimodal beta distribution, given the request that the p-quantile should be q, i.e. X ~ Be(a, b) with Pr(X <= q) = p.

Usage

getMinInfBeta(p, q)
getMinInfBeta(p, q)

Arguments

`p`	the probability (> 0 and < 1)
`q`	the quantile (> 0 and < 1)

Value

the two resulting beta parameters a and b in a list

Initialization function for "IncrementMin"

Description

Initialization function for "IncrementMin"

Usage

IncrementMin(IncrementsList)
IncrementMin(IncrementsList)

Arguments

IncrementsList

see IncrementMin

Value

the IncrementMin object

Max increment based on minimum of multiple increment rules

Description

This class can be used to combine multiple increment rules with the MIN operation.

Details

IncrementsList contains all increment rules, which are again objects of class Increments. The minimum of these individual increments is taken to give the final maximum increment.

Slots

IncrementsList: list of increment rules

Examples


# As example, here we are combining 2 different increment rules. 

# The first rule is the following: 
#      maximum doubling the dose if no DLTs were observed at the current dose
#      or maximum increasing the dose by 1.33 if 1 or 2 DLTs were observed at the current dose
#      or maximum increasing the dose by 1.22 if 3 or more DLTs were observed

# The second rule is the following: 
#   maximum doubling the dose if the current dose is <20
#   OR only maximum increasing the dose by 1.33 if the current dose is >=20


myIncrements1 <- IncrementsRelativeDLT(DLTintervals = c(0, 1, 3),
                                      increments = c(1, 0.33, 0.2))

myIncrements2 <- IncrementsRelative(intervals=c(0, 20),
                                    increments=c(1, 0.33))

# Now we combine the 2 rules
combIncrement <- IncrementMin(IncrementsList=
                                list(myIncrements1,myIncrements2))


# As example, here we are combining 2 different increment rules. 

# The first rule is the following: 
#      maximum doubling the dose if no DLTs were observed at the current dose
#      or maximum increasing the dose by 1.33 if 1 or 2 DLTs were observed at the current dose
#      or maximum increasing the dose by 1.22 if 3 or more DLTs were observed

# The second rule is the following: 
#   maximum doubling the dose if the current dose is <20
#   OR only maximum increasing the dose by 1.33 if the current dose is >=20


myIncrements1 <- IncrementsRelativeDLT(DLTintervals = c(0, 1, 3),
                                      increments = c(1, 0.33, 0.2))

myIncrements2 <- IncrementsRelative(intervals=c(0, 20),
                                    increments=c(1, 0.33))

# Now we combine the 2 rules
combIncrement <- IncrementMin(IncrementsList=
                                list(myIncrements1,myIncrements2))

The virtual class for controlling increments

Description

The virtual class for controlling increments

Initialization function for "IncrementsNumDoseLevels"

Description

Initialization function for "IncrementsNumDoseLevels"

Usage

IncrementsNumDoseLevels(maxLevels = 1)
IncrementsNumDoseLevels(maxLevels = 1)

Arguments

maxLevels

see IncrementsNumDoseLevels

Value

the IncrementsNumDoseLevels object

Increments control based on number of dose levels

Description

Increments control based on number of dose levels

Slots

maxLevels: scalar positive integer for the number of maximum dose levels to increment for the next dose. It defaults to 1, which means that no dose skipping is allowed - the next dose can be maximum one level higher than the current dose.

Examples


# As example, here is the rule for: 
# maximum skip one dose level, that is 2 dose levels higher is maximum
# increment
myIncrements <- IncrementsNumDoseLevels(maxLevels=2)

# As example, here is the rule for: 
# maximum skip one dose level, that is 2 dose levels higher is maximum
# increment
myIncrements <- IncrementsNumDoseLevels(maxLevels=2)

Initialization function for "IncrementsRelative"

Description

Initialization function for "IncrementsRelative"

Usage

IncrementsRelative(intervals, increments)
IncrementsRelative(intervals, increments)

Arguments

`intervals`	see `IncrementsRelative`
`increments`	see `IncrementsRelative`

Value

the IncrementsRelative object

Increments control based on relative differences in intervals

Description

Note that intervals is to be read as follows. If for example, we want to specify three intervals: First 0 to less than 50, second at least 50 up to less than 100 mg, and third at least 100 mg, then we specify intervals to be c(0, 50, 100). That means, the right bound of the intervals are exclusive to the interval, and the last interval goes from the last value until infinity.

Slots

intervals: a vector with the left bounds of the relevant intervals
increments: a vector of the same length with the maximum allowable relative increments in the intervals

Examples


# As example, here is the rule for: 
#   maximum doubling the dose if the current dose is <20
#   OR only maximum increasing the dose by 1.33 if the current dose is >=20

myIncrements <- IncrementsRelative(intervals=c(0, 20),
                                   increments=c(1, 0.33))

# As example, here is the rule for: 
#   maximum doubling the dose if the current dose is <20
#   OR only maximum increasing the dose by 1.33 if the current dose is >=20

myIncrements <- IncrementsRelative(intervals=c(0, 20),
                                   increments=c(1, 0.33))

Initialization function for "IncrementsRelativeDLT"

Description

Initialization function for "IncrementsRelativeDLT"

Usage

IncrementsRelativeDLT(DLTintervals, increments)
IncrementsRelativeDLT(DLTintervals, increments)

Arguments

`DLTintervals`	see `IncrementsRelativeDLT`
`increments`	see `IncrementsRelativeDLT`

Value

the IncrementsRelativeDLT object

Increments control based on relative differences in terms of DLTs

Description

Note that DLTintervals is to be read as follows. If for example, we want to specify three intervals: First 0 DLTs, second 1 or 2 DLTs, and third at least 3 DLTs, then we specify DLTintervals to be c(0, 1, 3). That means, the right bound of the intervals are exclusive to the interval – the vector only gives the left bounds of the intervals. The last interval goes from 3 to infinity.

Slots

DLTintervals: an integer vector with the left bounds of the relevant DLT intervals
increments: a vector of the same length with the maximum allowable relative increments in the DLTintervals

Examples


# As example, here is the rule for: 
#      maximum doubling the dose if no DLTs were observed at the current dose
#      or maximum increasing the dose by 1.33 if 1 or 2 DLTs were observed at the current dose
#      or maximum increasing the dose by 1.22 if 3 or more DLTs were observed

myIncrements <- IncrementsRelativeDLT(DLTintervals = c(0, 1, 3),
                                      increments = c(1, 0.33, 0.2))

# As example, here is the rule for: 
#      maximum doubling the dose if no DLTs were observed at the current dose
#      or maximum increasing the dose by 1.33 if 1 or 2 DLTs were observed at the current dose
#      or maximum increasing the dose by 1.22 if 3 or more DLTs were observed

myIncrements <- IncrementsRelativeDLT(DLTintervals = c(0, 1, 3),
                                      increments = c(1, 0.33, 0.2))

Initialization function for "IncrementsRelativeParts"

Description

Initialization function for "IncrementsRelativeParts"

Usage

IncrementsRelativeParts(dltStart, cleanStart, ...)
IncrementsRelativeParts(dltStart, cleanStart, ...)

Arguments

`dltStart`	see `IncrementsRelativeParts`
`cleanStart`	see `IncrementsRelativeParts`
`...`	additional slots from `IncrementsRelative`

Value

the IncrementsRelativeParts object

Increments control based on relative differences in intervals, with special rules for part 1 and beginning of part 2

Description

Note that this only works in conjunction with DataParts objects. If the part 2 will just be started in the next cohort, then the next maximum dose will be either dltStart (e.g. -1) shift of the last part 1 dose in case of a DLT in part 1, or cleanStart shift (e.g. 0) in case of no DLTs in part 1. If part 1 will still be on in the next cohort, then the next dose level will be the next higher dose level in the part1Ladder of the data object. If part 2 has been started before, the usual relative increment rules apply, see IncrementsRelative.

Slots

dltStart: integer giving the dose level increment for starting part 2 in case of a DLT in part 1
cleanStart: integer giving the dose level increment for starting part 2 in case of a DLT in part 1. If this is less or equal to 0, then the part 1 ladder will be used to find the maximum next dose. If this is larger than 0, then the relative increment rules will be applied to find the next maximum dose level.

Examples


myIncrements <- IncrementsRelativeParts(dltStart=0,
                                        cleanStart=1)

myIncrements <- IncrementsRelativeParts(dltStart=0,
                                        cleanStart=1)

Initialization method for the "DualEndpointOld" class

Description

Initialization method for the "DualEndpointOld" class

Usage

## S4 method for signature 'DualEndpointOld'
initialize(
  .Object,
  mu,
  Sigma,
  sigma2betaW,
  sigma2W,
  rho,
  smooth = c("RW1", "RW2"),
  ...
)
## S4 method for signature 'DualEndpointOld'
initialize(
  .Object,
  mu,
  Sigma,
  sigma2betaW,
  sigma2W,
  rho,
  smooth = c("RW1", "RW2"),
  ...
)

Arguments

`.Object`	the `DualEndpointOld` we want to initialize
`mu`	see `DualEndpointOld`
`Sigma`	see `DualEndpointOld`
`sigma2betaW`	see `DualEndpointOld`
`sigma2W`	see `DualEndpointOld`
`rho`	see `DualEndpointOld`
`smooth`	either “RW1” (default) or “RW2”, for specifying the random walk prior on the biomarker level.
`...`	not used

Intialization function for "LogisticIndepBeta" class

Description

Intialization function for "LogisticIndepBeta" class

Usage

LogisticIndepBeta(binDLE, DLEdose, DLEweights, data)
LogisticIndepBeta(binDLE, DLEdose, DLEweights, data)

Arguments

`binDLE`	the number of subjects observed with a DLE, the pseudo DLE responses
`DLEdose`	the corresponding dose levels for the pseudo DLE responses, pseudo dose levels
`DLEweights`	the total number of subjects treated at each of the dose levels, pseudo weights
`data`	the input data to update estimates of model parameters and follow the `Data` object class specification

Value

the LogisticIndepBeta

No initialization function Standard logistic model with prior in form of pseudo data

Description

This is a class for the two-parameter logistic regression DLE model with prior expressed in form of pseudo data. This model describe the relationhship of the binary DLE (dose-limiting events) responses and the dose levels. More specifically, this DLE model reprsents the relationship of the probabilities of the occurrence of a DLE with their corresponding dose levels in log scale. This model is specified as

$p(d_{(j)})= \frac{exp(\phi_1+\phi_2 log(d_{(j)}))}{1+exp(\phi_1+\phi_2 log(d_{(j)}))}$

for any dose j where $p(d_{(j)})$ is the probability of the occurrence of a DLE at dose j. The two parameters of this model is the intercept $\phi_1$ and the slope $\phi_2$ It inherits all slots from ModelTox class.

Details

The pseudo data can be interpreted as as if we obtain some observations before the trial starts. These pseudo data can be used to express our prior, the initial beliefs for the model parameter(s). The pseudo data are expressed in the following way. First, fix at least two dose levels which are Then ask for experts' opinion how many subjects are to be treated at each of these dose levels and the number of subjects observed with DLE are observed. At each dose level, the number of subjects observed with a DLE divided by the total number of subjects treated is the probability of the occurrence of a DLE at that particular dose level. The probabilities of the occurrence of a DLE based on these pseudo data are independent Beta distributions. Therefore, the joint prior probability density function of all these probabilities can be obtained. Hence, by a change of variable, the joint prior probability density function of the two parameters in this model can also be obtained. In addition, a conjugate joint perior density function of the two paramaters in the model is used. For details about the form of all these joint prior and posterior probability density function, please refers to Whitehead and Willamson (1998).

When expressing the pseudo data, binDLE,DLEdose and DLEweights are used. The binDLE represents the number of subjects observed with DLE. Note that, since the imaginary nature of the pseudo data, the number of subjects observed wtih DLE is not necesssary to be integer(s) but any scalar value. The DLEdose represents the dose levels at which the pseudo DLE responses (binDLE) are observed. The DLEweights represents the total number of subjects treated. Since at least two DLE pseudo responses are needed to obtain prior modal estimates (same as the maximum likelihood estimates) for the model parameters. binDLE, DLEdose and DLEweights must all be vectors of at least length 2. Since given one pseudo DLE responses, the number of subjects observed with a DLE relates to at which dose level they are treated and the total number of of subjects treated at this dose level. Therefore, each of the elements in any of the vectors of binDLE, DLEdose and DLEweights must have a corresponding elements in the other two vectors. A set of three values with one of each in the vectors of binDLE, DLEdose and DLEweights. In this model, each of these three values must be specified in the same position as in each of the vector of binDLE, DLEdose and DLEweights. The order of the values or elements in one of the vector binDLE, DLEdose and DLEweights must corresponds to the values or elements specified in the other two vectors.

Slots

binDLE: represents the vector of pseudo DLE responses. This must be at least f length 2 and the order of its elements must corresponds to values specified in DLEdose and DLEweights. (see details from above)
DLEdose: represents the vector of the corresponding dose levels observed at each of the pseudo DLE responses (binDLE). This mus be at least of length 2 and the order of its elements must corresponds to values specified in binDLE and DLEweights. (see details from above)
DLEweights: refers to the total number of subjects treated at each of the pseudo dose level (DLEdose). This must be of length of at least 2 and the oreder of its elements must corresponds to values specified in binDLE and DLEdose. (see details from above)
phi1: refers the intercept of the model. This slot is used in output to display the resulting prior or posterior modal estimate of the intercept obtained based on the pseudo data and (if any) observed data/responses.
phi2: refers to slope of the model. This slot is used in output to display the resulting prior or posterior modal estimate of the slope obtained based on the pseudo data and (if any) the observed data/responses.
Pcov: refers to the covariance matrix of the intercept (phi1) and the slope parameters (phi2) of the model. This is used in output to display the resulting prior and posterior covariance matrix of phi1 and phi2 obtained, based on the pseudo data and (if any) the observed data and responses. This slot is needed for internal purposes.

Examples

##Obtain prior modal estimates given the pseudo data.
##First we used an empty data set such that only the dose levels under investigations are given.
##In total, 12 dose levels are under investigation ranging from 25 to 300 mg with increments of 25
##(i.e 25, 50, 75, ..., 300).
emptydata<- Data(doseGrid=seq(25,300,25))

##specified our data set is the empty data
data<-emptydata
## Given the pseudo data such that
## Fix two dose level 25 and 300 mg and specified in (DLEdose slot).
## Total number of subjects treated in each of these levels is 3, specified in (DLEweights slot).
## The number of subjects observed with a DLE is 1.05 at dose 25 mg and 1.8 at dose 300 mg, 
## and specified in (binDLE slot).
## the data set we used in the emptydata set, and specified in (data slot).
## Then to modal estimates of the model parameters.
model<-LogisticIndepBeta(binDLE=c(1.05,1.8),DLEweights=c(3,3),DLEdose=c(25,300),data=data)

##using a data set (see data -class example specification) with observed DLE responses 
##to obtain posterior modal estimates. 
##for the model given the pseudo data

data<-Data(x=c(25,50,50,75,100,100,225,300),y=c(0,0,0,0,1,1,1,1),
           doseGrid=seq(25,300,25))

model<-LogisticIndepBeta(binDLE=c(1.05,1.8),DLEweights=c(3,3),DLEdose=c(25,300),data=data)

##Obtain prior modal estimates given the pseudo data.
##First we used an empty data set such that only the dose levels under investigations are given.
##In total, 12 dose levels are under investigation ranging from 25 to 300 mg with increments of 25
##(i.e 25, 50, 75, ..., 300).
emptydata<- Data(doseGrid=seq(25,300,25))

##specified our data set is the empty data
data<-emptydata
## Given the pseudo data such that
## Fix two dose level 25 and 300 mg and specified in (DLEdose slot).
## Total number of subjects treated in each of these levels is 3, specified in (DLEweights slot).
## The number of subjects observed with a DLE is 1.05 at dose 25 mg and 1.8 at dose 300 mg, 
## and specified in (binDLE slot).
## the data set we used in the emptydata set, and specified in (data slot).
## Then to modal estimates of the model parameters.
model<-LogisticIndepBeta(binDLE=c(1.05,1.8),DLEweights=c(3,3),DLEdose=c(25,300),data=data)

##using a data set (see data -class example specification) with observed DLE responses 
##to obtain posterior modal estimates. 
##for the model given the pseudo data

data<-Data(x=c(25,50,50,75,100,100,225,300),y=c(0,0,0,0,1,1,1,1),
           doseGrid=seq(25,300,25))

model<-LogisticIndepBeta(binDLE=c(1.05,1.8),DLEweights=c(3,3),DLEdose=c(25,300),data=data)

Initialization function for the "LogisticKadane" class

Description

Initialization function for the "LogisticKadane" class

Usage

LogisticKadane(theta, xmin, xmax)
LogisticKadane(theta, xmin, xmax)

Arguments

`theta`	the target toxicity probability
`xmin`	the minimum of the dose range
`xmax`	the maximum of the dose range

Value

the LogisticKadane

Reparametrized logistic model

Description

This is the logistic model in the parametrization of Kadane et al. (1980).

Details

Let $\rho_{0} = p(x_{min})$ be the probability of a DLT and the minimum dose $x_{min}$ , and let $\gamma$ be the dose with target toxicity probability $\theta$ , i.e. $p(\gamma) = \theta$ . Then it can easily be shown that the logistic regression model has intercept

$\frac{\gamma logit(\rho_{0}) - x_{min} logit(\theta)}{\gamma - x_{min}}$

and slope

$\frac{logit(theta) - logit(\rho_{0})}{\gamma - x_{min}}$

The prior is a uniform distribution for $\gamma$ between $x_{min}$ and $x_{max}$ , and for $\rho_{0}$ as well a uniform distribution between $0$ and $\theta$ .

The slots of this class, required for creating the model, are the target toxicity, as well as the minimum and maximum of the dose range. Note that these can be different from the minimum and maximum of the dose grid in the data later on.

Slots

theta: the target toxicity probability $\theta$
xmin: the minimum of the dose range $x_{min}$
xmax: the maximum of the dose range $x_{max}$

Examples



model <- LogisticKadane(theta = 0.33,
                        xmin = 1,
                        xmax = 200)


model <- LogisticKadane(theta = 0.33,
                        xmin = 1,
                        xmax = 200)

Initialization function for the "LogisticLogNormal" class

Description

Initialization function for the "LogisticLogNormal" class

Usage

LogisticLogNormal(mean, cov, refDose)
LogisticLogNormal(mean, cov, refDose)

Arguments

`mean`	the prior mean vector
`cov`	the prior covariance matrix
`refDose`	the reference dose

Value

the LogisticLogNormal object

Standard logistic model with bivariate (log) normal prior

Description

This is the usual logistic regression model with a bivariate normal prior on the intercept and log slope.

Details

The covariate is the natural logarithm of the dose $x$ divided by the reference dose $x^{*}$ :

$logit[p(x)] = \alpha + \beta \cdot \log(x/x^{*})$

where $p(x)$ is the probability of observing a DLT for a given dose $x$ .

The prior is

$(\alpha, \log(\beta)) \sim Normal(\mu, \Sigma)$

The slots of this class contain the mean vector and the covariance matrix of the bivariate normal distribution, as well as the reference dose.

Note that the parametrization inside the class uses alpha0 and alpha1. alpha0 is identical to the intercept $\alpha$ above and is the log-odds for a DLT at the reference dose x*. Therefore, the prior mean for alpha0 is the expected log-odds at the reference dose x* before observing any data. Note that the expected odds is not just the exp of the prior mean of alpha0, because the non-linearity of the exp transformation. The log-normal distribution on Wikipedia gives the formula for computing the prior mean of exp(alpha0). alpha0 is the log(alpha) in the Neuenschwander et al. (2008) paper. alpha1 is identical to $\beta$ above and equals the beta in the Neuenschwander et al paper. exp(alpha1) gives the odds-ratio for DLT between two doses that differ by the factor exp(1) ~ 2.7. alpha1 has a log-normal distribution in the LogisticLogNormal model in order to ensure positivity of alpha1 and thus exp(alpha1) > 1.

Slots

mean: the prior mean vector $\mu$
cov: the prior covariance matrix $\Sigma$
refDose: the reference dose $x^{*}$

Examples



model <- LogisticLogNormal(mean = c(-0.85, 1),
                           cov = matrix(c(1, -0.5, -0.5, 1), nrow = 2),
                           refDose = 50)


model <- LogisticLogNormal(mean = c(-0.85, 1),
                           cov = matrix(c(1, -0.5, -0.5, 1), nrow = 2),
                           refDose = 50)

Initialization function for the "LogisticLogNormalMixture" class

Description

Initialization function for the "LogisticLogNormalMixture" class

Usage

LogisticLogNormalMixture(mean, cov, refDose, shareWeight)
LogisticLogNormalMixture(mean, cov, refDose, shareWeight)

Arguments

`mean`	the prior mean vector
`cov`	the prior covariance matrix
`refDose`	the reference dose
`shareWeight`	the prior weight for the share component

Value

the LogisticLogNormalMixture object

Standard logistic model with online mixture of two bivariate log normal priors

Description

This model can be used when data is arising online from the informative component of the prior, at the same time with the data of the trial of main interest. Formally, this is achieved by assuming that the probability of a DLT at dose $x$ is given by

Details

$p(x) = \pi p_{1}(x) + (1 - \pi) p_{2}(x)$

where $\pi$ is the probability for the model $p(x)$ being the same as the model $p_{1}(x)$ - this is the informative component of the prior. From this model data arises in parallel: at doses xshare, DLT information yshare is observed, in total nObsshare data points, see DataMixture. On the other hand, $1 - \pi$ is the probability of a separate model $p_{2}(x)$ . Both components have the same log normal prior distribution, which can be specified by the user, and which is inherited from the LogisticLogNormal class.

Slots

shareWeight: the prior weight for sharing the same model $p_{1}(x)$

Examples

## decide on the dose grid:
doseGrid <- 1:80

## and MCMC options:
options <- McmcOptions()

## the classic model would be:
model <- LogisticLogNormal(mean = c(-0.85, 1),
                           cov = matrix(c(1, -0.5, -0.5, 1), nrow = 2),
                           refDose = 50)

nodata <- Data(doseGrid=doseGrid)

priorSamples <- mcmc(nodata, model, options)
plot(priorSamples, model, nodata)

## set up the mixture model and data share object:
modelShare <- LogisticLogNormalMixture(shareWeight=0.1,
                                       mean = c(-0.85, 1),
                                       cov = matrix(c(1, -0.5, -0.5, 1), nrow = 2),
                                       refDose = 50)

nodataShare <- DataMixture(doseGrid=doseGrid,
                           xshare=
                             c(rep(10, 4),
                               rep(20, 4),
                               rep(40, 4)),
                           yshare=
                             c(rep(0L, 4),
                               rep(0L, 4),
                               rep(0L, 4)))

## now compare with the resulting prior model:
priorSamplesShare <- mcmc(nodataShare, modelShare, options)
plot(priorSamplesShare, modelShare, nodataShare)
## decide on the dose grid:
doseGrid <- 1:80

## and MCMC options:
options <- McmcOptions()

## the classic model would be:
model <- LogisticLogNormal(mean = c(-0.85, 1),
                           cov = matrix(c(1, -0.5, -0.5, 1), nrow = 2),
                           refDose = 50)

nodata <- Data(doseGrid=doseGrid)

priorSamples <- mcmc(nodata, model, options)
plot(priorSamples, model, nodata)

## set up the mixture model and data share object:
modelShare <- LogisticLogNormalMixture(shareWeight=0.1,
                                       mean = c(-0.85, 1),
                                       cov = matrix(c(1, -0.5, -0.5, 1), nrow = 2),
                                       refDose = 50)

nodataShare <- DataMixture(doseGrid=doseGrid,
                           xshare=
                             c(rep(10, 4),
                               rep(20, 4),
                               rep(40, 4)),
                           yshare=
                             c(rep(0L, 4),
                               rep(0L, 4),
                               rep(0L, 4)))

## now compare with the resulting prior model:
priorSamplesShare <- mcmc(nodataShare, modelShare, options)
plot(priorSamplesShare, modelShare, nodataShare)

Initialization function for the "LogisticLogNormalSub" class

Description

Initialization function for the "LogisticLogNormalSub" class

Usage

LogisticLogNormalSub(mean, cov, refDose)
LogisticLogNormalSub(mean, cov, refDose)

Arguments

`mean`	the prior mean vector
`cov`	the prior covariance matrix
`refDose`	the reference dose

Value

the LogisticLogNormalSub object

Standard logistic model with bivariate (log) normal prior with substractive dose standardization

Description

This is the usual logistic regression model with a bivariate normal prior on the intercept and log slope.

Details

The covariate is the dose $x$ minus the reference dose $x^{*}$ :

$logit[p(x)] = \alpha + \beta \cdot (x - x^{*})$

where $p(x)$ is the probability of observing a DLT for a given dose $x$ .

The prior is

$(\alpha, \log(\beta)) \sim Normal(\mu, \Sigma)$

The slots of this class contain the mean vector and the covariance matrix of the bivariate normal distribution, as well as the reference dose.

Slots

mean: the prior mean vector $\mu$
cov: the prior covariance matrix $\Sigma$
refDose: the reference dose $x^{*}$

Examples



model <- LogisticLogNormalSub(mean = c(-0.85, 1),
                           cov = matrix(c(1, -0.5, -0.5, 1), nrow = 2),
                           refDose = 50)


model <- LogisticLogNormalSub(mean = c(-0.85, 1),
                           cov = matrix(c(1, -0.5, -0.5, 1), nrow = 2),
                           refDose = 50)

Initialization function for the "LogisticNormal" class

Description

Initialization function for the "LogisticNormal" class

Usage

LogisticNormal(mean, cov, refDose)
LogisticNormal(mean, cov, refDose)

Arguments

`mean`	the prior mean vector
`cov`	the prior covariance matrix
`refDose`	the reference dose

Value

the LogisticNormal object

Standard logistic model with bivariate normal prior

Description

This is the usual logistic regression model with a bivariate normal prior on the intercept and slope.

Details

The covariate is the natural logarithm of the dose $x$ divided by the reference dose $x^{*}$ :

$logit[p(x)] = \alpha + \beta \cdot \log(x/x^{*})$

where $p(x)$ is the probability of observing a DLT for a given dose $x$ .

The prior is

$(\alpha, \beta) \sim Normal(\mu, \Sigma)$

The slots of this class contain the mean vector, the covariance and precision matrices of the bivariate normal distribution, as well as the reference dose.

Slots

mean: the prior mean vector $\mu$
cov: the prior covariance matrix $\Sigma$
prec: the prior precision matrix $\Sigma^{-1}$
refDose: the reference dose $x^{*}$

Examples

# Define the dose-grid
emptydata <- Data(doseGrid = c(1, 3, 5, 10, 15, 20, 25, 40, 50, 80, 100))


model <- LogisticNormal(mean = c(-0.85, 1),
                        cov = matrix(c(1, -0.5, -0.5, 1), nrow = 2),
                        refDose = 50)

options <- McmcOptions(burnin=100,
                       step=2,
                       samples=1000)

options(error=recover)
mcmc(emptydata, model, options)
# Define the dose-grid
emptydata <- Data(doseGrid = c(1, 3, 5, 10, 15, 20, 25, 40, 50, 80, 100))


model <- LogisticNormal(mean = c(-0.85, 1),
                        cov = matrix(c(1, -0.5, -0.5, 1), nrow = 2),
                        refDose = 50)

options <- McmcOptions(burnin=100,
                       step=2,
                       samples=1000)

options(error=recover)
mcmc(emptydata, model, options)

Initialization function for the "LogisticNormalFixedMixture" class

Description

Initialization function for the "LogisticNormalFixedMixture" class

Usage

LogisticNormalFixedMixture(components, weights, refDose, logNormal = FALSE)
LogisticNormalFixedMixture(components, weights, refDose, logNormal = FALSE)

Arguments

`components`	the specifications of the mixture components: a list with one list of `mean` and `cov` for each bivariate (log) normal prior
`weights`	the weights of the components, these must be positive and will be normalized to sum to 1
`refDose`	the reference dose
`logNormal`	should a log normal prior be specified, such that the mean vectors and covariance matrices are valid for the intercept and log slope? (not default)

Value

the LogisticNormalFixedMixture object

Standard logistic model with fixed mixture of multiple bivariate (log) normal priors

Description

This is standard logistic regression model with a mixture of multiple bivariate (log) normal priors on the intercept and slope parameters. The weights of the normal priors are fixed, hence no additional model parameters are introduced. This type of prior is often used to better approximate a given posterior distribution, or when the information is given in terms of a mixture.

Details

The covariate is the natural logarithm of the dose $x$ divided by the reference dose $x^{*}$ :

$logit[p(x)] = \alpha + \beta \cdot \log(x/x^{*})$

where $p(x)$ is the probability of observing a DLT for a given dose $x$ .

The prior is

$(\alpha, \beta) \sim \sum_{j=1}^{K} w_{j} Normal(\mu_{j}, \Sigma_{j})$

if a normal prior is used and

$(\alpha, \log(\beta)) \sim \sum_{j=1}^{K} w_{j} Normal(\mu_{j}, \Sigma_{j})$

if a log normal prior is used.

The weight $w_{j}$ of the components are fixed and sum to 1.

The (additional) slots of this class comprise two lists, containing the mean vector, the covariance and precision matrices of the two bivariate normal distributions each, the parameters of the beta prior for the first component weight, as well as the reference dose. Moreover, a slot specifies whether a log normal prior is used.

Slots

components: a list with one entry per component of the mixture. Each entry is a list with mean, cov and prec for the bivariate normal prior
weights: the weights of the components, these must be positive and sum to 1
refDose: the reference dose $x^{*}$
logNormal: is a log normal prior specified for each of the components?

Examples


model <- LogisticNormalFixedMixture(components = 
                                      list(comp1 = list(mean = c(-0.85, 1),
                                                        cov = matrix(c(1, -0.5, -0.5, 1), 
                                                                     nrow = 2)),
                                           comp2 = list(mean = c(1, 1.5),
                                                        cov = matrix(c(1.2, -0.45, -0.45, 0.6), 
                                                                     nrow = 2))),
                                    weights = c(0.3,0.7),
                                    refDose = 50)


model <- LogisticNormalFixedMixture(components = 
                                      list(comp1 = list(mean = c(-0.85, 1),
                                                        cov = matrix(c(1, -0.5, -0.5, 1), 
                                                                     nrow = 2)),
                                           comp2 = list(mean = c(1, 1.5),
                                                        cov = matrix(c(1.2, -0.45, -0.45, 0.6), 
                                                                     nrow = 2))),
                                    weights = c(0.3,0.7),
                                    refDose = 50)

Initialization function for the "LogisticNormalMixture" class

Description

Initialization function for the "LogisticNormalMixture" class

Usage

LogisticNormalMixture(comp1, comp2, weightpar, refDose)
LogisticNormalMixture(comp1, comp2, weightpar, refDose)

Arguments

`comp1`	the specifications of the first component: a list with `mean` and `cov` for the first bivariate normal prior
`comp2`	the specifications of the second component
`weightpar`	the beta parameters for the weight of the first component
`refDose`	the reference dose

Value

the LogisticNormalMixture object

Standard logistic model with flexible mixture of two bivariate normal priors

Description

This is standard logistic regression model with a mixture of two bivariate normal priors on the intercept and slope parameters. The weight of the two normal priors is a model parameter, hence it is a flexible mixture. This type of prior is often used with a mixture of a minimal informative and an informative component, in order to make the CRM more robust to data deviations from the informative component.

Details

The covariate is the natural logarithm of the dose $x$ divided by the reference dose $x^{*}$ :

$logit[p(x)] = \alpha + \beta \cdot \log(x/x^{*})$

where $p(x)$ is the probability of observing a DLT for a given dose $x$ .

The prior is

$(\alpha, \beta) \sim w * Normal(\mu_{1}, \Sigma_{1}) + (1 - w) * Normal(\mu_{2}, \Sigma_{2})$

The weight w for the first component is assigned a beta prior B(a, b).

The slots of this class comprise two lists, containing the mean vector, the covariance and precision matrices of the two bivariate normal distributions each, the parameters of the beta prior for the first component weight, as well as the reference dose.

Slots

comp1: the specifications of the first component: a list with mean, cov and prec for the first bivariate normal prior
comp2: the specifications of the second component
weightpar: the beta parameters for the weight of the first component
refDose: the reference dose $x^{*}$

Examples


model <- LogisticNormalMixture(comp1 = list(mean = c(-0.85, 1),
                                            cov = matrix(c(1, -0.5, -0.5, 1), 
                                                         nrow = 2)),
                               comp2 = list(mean = c(1, 1.5),
                                            cov = matrix(c(1.2, -0.45, -0.45, 0.6), 
                                                         nrow = 2)),
                               weightpar = c(a=1, b=1),
                               refDose = 50)


model <- LogisticNormalMixture(comp1 = list(mean = c(-0.85, 1),
                                            cov = matrix(c(1, -0.5, -0.5, 1), 
                                                         nrow = 2)),
                               comp2 = list(mean = c(1, 1.5),
                                            cov = matrix(c(1.2, -0.45, -0.45, 0.6), 
                                                         nrow = 2)),
                               weightpar = c(a=1, b=1),
                               refDose = 50)

Shorthand for logit function

Description

Shorthand for logit function

Usage

logit(x)
logit(x)

Arguments

`x`	the function argument

Value

the logit(x)

Helper function for value matching with tolerance

Description

This is a modified version of match that supports tolerance.

Usage

matchTolerance(x, table)

x %~% table
matchTolerance(x, table)

x %~% table

Arguments

`x`	the values to be matched
`table`	the values to be matched against

Value

A vector of the same length as x

Functions

x %~% table: Helper function for checking inclusion in a table with tolerance

Examples

myDose <- c(rep(0.030, 6), rep(0.050, 3), rep(0.075, 4), rep(0.1, 9), rep(0.15, 7))
doseGrid <- seq(from = .025, to = .15, by = .005)

myDose %in% doseGrid
matchTolerance(myDose, doseGrid)
myDose %~% doseGrid

matchTolerance(c(myDose, 500), doseGrid)
c(myDose, 500) %~% doseGrid
myDose <- c(rep(0.030, 6), rep(0.050, 3), rep(0.075, 4), rep(0.1, 9), rep(0.15, 7))
doseGrid <- seq(from = .025, to = .15, by = .005)

myDose %in% doseGrid
matchTolerance(myDose, doseGrid)
myDose %~% doseGrid

matchTolerance(c(myDose, 500), doseGrid)
c(myDose, 500) %~% doseGrid

Determine the maximum possible next dose

Description

Determine the upper limit of the next dose based on the increments rule.

Usage

maxDose(increments, data, ...)

## S4 method for signature 'IncrementsRelative,Data'
maxDose(increments, data, ...)

## S4 method for signature 'IncrementsNumDoseLevels,Data'
maxDose(increments, data, ...)

## S4 method for signature 'IncrementsRelativeParts,DataParts'
maxDose(increments, data, ...)

## S4 method for signature 'IncrementsRelativeDLT,Data'
maxDose(increments, data, ...)

## S4 method for signature 'IncrementMin,Data'
maxDose(increments, data, ...)
maxDose(increments, data, ...)

## S4 method for signature 'IncrementsRelative,Data'
maxDose(increments, data, ...)

## S4 method for signature 'IncrementsNumDoseLevels,Data'
maxDose(increments, data, ...)

## S4 method for signature 'IncrementsRelativeParts,DataParts'
maxDose(increments, data, ...)

## S4 method for signature 'IncrementsRelativeDLT,Data'
maxDose(increments, data, ...)

## S4 method for signature 'IncrementMin,Data'
maxDose(increments, data, ...)

Arguments

`increments`	The rule, an object of class `Increments`
`data`	The data input, an object of class `Data`
`...`	further arguments

Details

This function outputs the maximum possible next dose, based on the corresponding rule increments and the data.

Value

the maximum possible next dose

Functions

maxDose(increments = IncrementsRelative, data = Data): Determine the maximum possible next dose based on relative increments
maxDose(increments = IncrementsNumDoseLevels, data = Data): Determine the maximum possible next dose based on maximum dose levels to increment for the next dose
maxDose(increments = IncrementsRelativeParts, data = DataParts): Determine the maximum possible next dose based on relative increments and part 1 and 2
maxDose(increments = IncrementsRelativeDLT, data = Data): Determine the maximum possible next dose based on relative increments determined by DLTs so far
maxDose(increments = IncrementMin, data = Data): Determine the maximum possible next dose based on multiple increment rules (taking the minimum across individual increments).

Examples


# Create the data
data <- Data(x=c(0.1, 0.5, 1.5, 3, 6, 8, 8, 8),
             y=c(0, 0, 0, 0, 0, 0, 1, 0),
             cohort=c(0, 1, 2, 3, 4, 5, 5, 5),
             doseGrid=
               c(0.1, 0.5, 1.5, 3, 6, 8,
                 seq(from=10, to=80, by=2)))


# In this example we define a rule for dose increments which would allow:
#   - doubling the dose if the last dose was below 20
#   - only increasing the dose by 1.33 if the last dose was equal or above 20
myIncrements <- IncrementsRelative(intervals=c(0, 20),
                                   increments=c(1, 0.33))

# Based on the rule above, we then calculate the maximum dose allowed
nextMaxDose <- maxDose(myIncrements,
                       data=data)


# Create the data
data <- Data(x=c(0.1, 0.5, 1.5, 3, 6, 8, 8, 8),
             y=c(0, 0, 0, 0, 0, 0, 1, 0),
             cohort=c(0, 1, 2, 3, 4, 5, 5, 5),
             doseGrid=
               c(0.1, 0.5, 1.5, 3, 6, 8,
                 seq(from=10, to=80, by=2)))


# In this example we define a rule for dose increments which would allow:
# maximum skip one dose level, that is 2 dose levels higher is maximum
# increment
myIncrements <- IncrementsNumDoseLevels(maxLevels=2)

# Based on the rule above, we then calculate the maximum dose allowed
nextMaxDose <- maxDose(myIncrements,
                       data=data)


# create an object of class 'DataParts'
myData <- DataParts(x=c(0.1,0.5,1.5),
                    y=c(0,0,0),
                    doseGrid=c(0.1,0.5,1.5,3,6,
                               seq(from=10,to=80,by=2)),
                    part=c(1L,1L,1L),
                    nextPart=1L,
                    part1Ladder=c(0.1,0.5,1.5,3,6,10))


myIncrements <- IncrementsRelativeParts(dltStart=0,
                                        cleanStart=1)

nextMaxDose <- maxDose(myIncrements,
                       data=myData)


# Create the data
data <- Data(x=c(0.1, 0.5, 1.5, 3, 6, 10, 10, 10),
             y=c(0, 0, 0, 0, 0, 0, 1, 0),
             cohort=c(0, 1, 2, 3, 4, 5, 5, 5),
             doseGrid=
               c(0.1, 0.5, 1.5, 3, 6,
                 seq(from=10, to=80, by=2)))


# In this example we define a rule for dose increments which would allow:
#   - doubling the dose if no DLTs were yet observed
#   - only increasing the dose by 1.33 if 1 or 2 DLTs were already observed
#   - only increasing the dose by 1.2 if at least 3 DLTs were already observed
myIncrements <- IncrementsRelativeDLT(DLTintervals = c(0, 1, 3),
                                      increments = c(1, 0.33, 0.2))

# Based on the rule above, we then calculate the maximum dose allowed
nextMaxDose <- maxDose(myIncrements,
                       data=data)


# Create the data
data <- Data(x=c(0.1, 0.5, 1.5, 3, 6, 8, 8, 8),
             y=c(0, 0, 0, 0, 0, 0, 1, 0),
             cohort=c(0, 1, 2, 3, 4, 5, 5, 5),
             doseGrid=
               c(0.1, 0.5, 1.5, 3, 6, 8,
                 seq(from=10, to=80, by=2)))


# As example, here we are combining 2 different increment rules. 

# The first rule is the following: 
#      maximum doubling the dose if no DLTs were observed at the current dose
#      or maximum increasing the dose by 1.33 if 1 or 2 DLTs were observed at the current dose
#      or maximum increasing the dose by 1.22 if 3 or more DLTs were observed

# The second rule is the following: 
#   maximum doubling the dose if the current dose is <20
#   OR only maximum increasing the dose by 1.33 if the current dose is >=20

myIncrements1 <- IncrementsRelativeDLT(DLTintervals = c(0, 1, 3),
                                       increments = c(1, 0.33, 0.2))

myIncrements2 <- IncrementsRelative(intervals=c(0, 20),
                                    increments=c(1, 0.33))

# Now we combine the 2 rules
combIncrement <- IncrementMin(IncrementsList=
                                list(myIncrements1,myIncrements2))

# Finally we then calculate the maximum dose allowed by taking the minimum of the two rules
nextMaxDose <- maxDose(combIncrement, 
                       data)

# Create the data
data <- Data(x=c(0.1, 0.5, 1.5, 3, 6, 8, 8, 8),
             y=c(0, 0, 0, 0, 0, 0, 1, 0),
             cohort=c(0, 1, 2, 3, 4, 5, 5, 5),
             doseGrid=
               c(0.1, 0.5, 1.5, 3, 6, 8,
                 seq(from=10, to=80, by=2)))


# In this example we define a rule for dose increments which would allow:
#   - doubling the dose if the last dose was below 20
#   - only increasing the dose by 1.33 if the last dose was equal or above 20
myIncrements <- IncrementsRelative(intervals=c(0, 20),
                                   increments=c(1, 0.33))

# Based on the rule above, we then calculate the maximum dose allowed
nextMaxDose <- maxDose(myIncrements,
                       data=data)


# Create the data
data <- Data(x=c(0.1, 0.5, 1.5, 3, 6, 8, 8, 8),
             y=c(0, 0, 0, 0, 0, 0, 1, 0),
             cohort=c(0, 1, 2, 3, 4, 5, 5, 5),
             doseGrid=
               c(0.1, 0.5, 1.5, 3, 6, 8,
                 seq(from=10, to=80, by=2)))


# In this example we define a rule for dose increments which would allow:
# maximum skip one dose level, that is 2 dose levels higher is maximum
# increment
myIncrements <- IncrementsNumDoseLevels(maxLevels=2)

# Based on the rule above, we then calculate the maximum dose allowed
nextMaxDose <- maxDose(myIncrements,
                       data=data)


# create an object of class 'DataParts'
myData <- DataParts(x=c(0.1,0.5,1.5),
                    y=c(0,0,0),
                    doseGrid=c(0.1,0.5,1.5,3,6,
                               seq(from=10,to=80,by=2)),
                    part=c(1L,1L,1L),
                    nextPart=1L,
                    part1Ladder=c(0.1,0.5,1.5,3,6,10))


myIncrements <- IncrementsRelativeParts(dltStart=0,
                                        cleanStart=1)

nextMaxDose <- maxDose(myIncrements,
                       data=myData)


# Create the data
data <- Data(x=c(0.1, 0.5, 1.5, 3, 6, 10, 10, 10),
             y=c(0, 0, 0, 0, 0, 0, 1, 0),
             cohort=c(0, 1, 2, 3, 4, 5, 5, 5),
             doseGrid=
               c(0.1, 0.5, 1.5, 3, 6,
                 seq(from=10, to=80, by=2)))


# In this example we define a rule for dose increments which would allow:
#   - doubling the dose if no DLTs were yet observed
#   - only increasing the dose by 1.33 if 1 or 2 DLTs were already observed
#   - only increasing the dose by 1.2 if at least 3 DLTs were already observed
myIncrements <- IncrementsRelativeDLT(DLTintervals = c(0, 1, 3),
                                      increments = c(1, 0.33, 0.2))

# Based on the rule above, we then calculate the maximum dose allowed
nextMaxDose <- maxDose(myIncrements,
                       data=data)


# Create the data
data <- Data(x=c(0.1, 0.5, 1.5, 3, 6, 8, 8, 8),
             y=c(0, 0, 0, 0, 0, 0, 1, 0),
             cohort=c(0, 1, 2, 3, 4, 5, 5, 5),
             doseGrid=
               c(0.1, 0.5, 1.5, 3, 6, 8,
                 seq(from=10, to=80, by=2)))


# As example, here we are combining 2 different increment rules. 

# The first rule is the following: 
#      maximum doubling the dose if no DLTs were observed at the current dose
#      or maximum increasing the dose by 1.33 if 1 or 2 DLTs were observed at the current dose
#      or maximum increasing the dose by 1.22 if 3 or more DLTs were observed

# The second rule is the following: 
#   maximum doubling the dose if the current dose is <20
#   OR only maximum increasing the dose by 1.33 if the current dose is >=20

myIncrements1 <- IncrementsRelativeDLT(DLTintervals = c(0, 1, 3),
                                       increments = c(1, 0.33, 0.2))

myIncrements2 <- IncrementsRelative(intervals=c(0, 20),
                                    increments=c(1, 0.33))

# Now we combine the 2 rules
combIncrement <- IncrementMin(IncrementsList=
                                list(myIncrements1,myIncrements2))

# Finally we then calculate the maximum dose allowed by taking the minimum of the two rules
nextMaxDose <- maxDose(combIncrement, 
                       data)

"MAX" combination of cohort size rules

Description

This function combines cohort size rules by taking the maximum of all sizes.

Usage

maxSize(...)

## S4 method for signature 'CohortSize'
maxSize(...)
maxSize(...)

## S4 method for signature 'CohortSize'
maxSize(...)

Arguments

...

Objects of class CohortSize

Value

the combination as an object of class CohortSizeMax

Functions

maxSize(CohortSize): The method combining cohort size rules by taking maximum

Examples


# Here is the rule for: 
#   having cohort of size 1 for doses <30
#   and having cohort of size 3 for doses >=30
mySize1 <- CohortSizeRange(intervals = c(0, 30),
                           cohortSize = c(1, 3))

# Here is the rule for: 
#   having cohort of size 1 until no DLT were observed
#   and having cohort of size 3 as soon as 1 DLT is observed
mySize2 <- CohortSizeDLT(DLTintervals=c(0, 1),
                         cohortSize=c(1, 3))

# This is combining the two rules above by taking the maximum of the sample sizes of
# the single rules
mySize <- maxSize(mySize1, mySize2)

# Here is the rule for: 
#   having cohort of size 1 for doses <30
#   and having cohort of size 3 for doses >=30
mySize1 <- CohortSizeRange(intervals = c(0, 30),
                           cohortSize = c(1, 3))

# Here is the rule for: 
#   having cohort of size 1 until no DLT were observed
#   and having cohort of size 3 as soon as 1 DLT is observed
mySize2 <- CohortSizeDLT(DLTintervals=c(0, 1),
                         cohortSize=c(1, 3))

# This is combining the two rules above by taking the maximum of the sample sizes of
# the single rules
mySize <- maxSize(mySize1, mySize2)

Obtain posterior samples for all model parameters

Description

This is the function to actually run the MCMC machinery to produce posterior samples from all model parameters and required derived values. It is a generic function, so that customized versions may be conveniently defined for specific subclasses of GeneralData, GeneralModel, and McmcOptions input.

Usage

mcmc(data, model, options, ...)

## S4 method for signature 'GeneralData,GeneralModel,McmcOptions'
mcmc(
  data,
  model,
  options,
  program = c("JAGS"),
  verbose = FALSE,
  fromPrior = data@nObs == 0L,
  ...
)

## S4 method for signature 'DataMixture,GeneralModel,McmcOptions'
mcmc(
  data,
  model,
  options,
  fromPrior = data@nObs == 0L & data@nObsshare == 0L,
  ...
)

## S4 method for signature 'Data,LogisticIndepBeta,McmcOptions'
mcmc(data, model, options, ...)

## S4 method for signature 'DataDual,Effloglog,McmcOptions'
mcmc(data, model, options, ...)

## S4 method for signature 'DataDual,EffFlexi,McmcOptions'
mcmc(data, model, options, ...)
mcmc(data, model, options, ...)

## S4 method for signature 'GeneralData,GeneralModel,McmcOptions'
mcmc(
  data,
  model,
  options,
  program = c("JAGS"),
  verbose = FALSE,
  fromPrior = data@nObs == 0L,
  ...
)

## S4 method for signature 'DataMixture,GeneralModel,McmcOptions'
mcmc(
  data,
  model,
  options,
  fromPrior = data@nObs == 0L & data@nObsshare == 0L,
  ...
)

## S4 method for signature 'Data,LogisticIndepBeta,McmcOptions'
mcmc(data, model, options, ...)

## S4 method for signature 'DataDual,Effloglog,McmcOptions'
mcmc(data, model, options, ...)

## S4 method for signature 'DataDual,EffFlexi,McmcOptions'
mcmc(data, model, options, ...)

Arguments

`data`	The data input, an object of class `GeneralData`
`model`	The model input, an object of class `GeneralModel`
`options`	MCMC options, an object of class `McmcOptions`
`...`	unused
`program`	the program which shall be used: currently only “JAGS” is supported
`verbose`	shall progress bar and messages be printed? (not default)
`fromPrior`	sample from the prior only? Defaults to checking if nObs is 0. For some models it might be necessary to specify it manually here though.

Details

Reproducible samples can be obtained by setting the seed via set.seed before in the user code as usual. However, note that because the RNG sampler used is external to R, running this MCMC function will not change the seed position – that is, the repeated call to this function will then result in exactly the same output.

Value

The posterior samples, an object of class Samples.

Functions

mcmc(data = GeneralData, model = GeneralModel, options = McmcOptions): Standard method which uses JAGS
mcmc(data = DataMixture, model = GeneralModel, options = McmcOptions): Method for DataMixture with different fromPrior default
mcmc(data = Data, model = LogisticIndepBeta, options = McmcOptions): Obtain posterior samples for the model parameters based on the pseudo 'LogisticsIndepBeta' DLE model. The joint prior and posterior probability density function of the intercept $\phi_1$ (phi1) and the slope $\phi_2$ (phi2) are given in Whitehead and Williamson (1998) and TsuTakawa (1975). However, since asymptotically, the joint posterior probability density will be bivariate normal and we will use the bivariate normal distribution to generate posterior samples of the intercept and the slope parameters. For the prior samples of of the intercept and the slope a bivariate normal distribution with mean and the covariance matrix given in Whitehead and Williamson (1998) is used.
mcmc(data = DataDual, model = Effloglog, options = McmcOptions): Obtain the posterior samples for the model parameters in the Efficacy log log model. Given the value of $\nu$ , the precision of the efficacy responses, the joint prior or the posterior probability of the intercept $\theta_1$ (theta1) and the slope $\theta_2$ (theta2) is a bivariate normal distribtuion. The $\nu$ (nu), the precision of the efficacy responses is either a fixed value or has a gamma distribution. If a gamma distribution is used, the samples of nu will be first generated. Then the mean of the of the nu samples will be used the generate samples of the intercept and slope parameters of the model
mcmc(data = DataDual, model = EffFlexi, options = McmcOptions): Obtain the posterior samples for the estimates in the Efficacy Flexible form. This is the mcmc procedure based on what is described in Lang and Brezger (2004) such that samples of the mean efficacy responses at all dose levels, samples of sigma2 $sigma^2$ , the variance of the efficacy response and samples of sigma2betaW $sigma^2_{beta_W}$ , the variance of the random walk model will be generated. Please refer to Lang and Brezger (2004) for the procedures and the form of the joint prior and posterior probability density for the mean efficay responses. In addition, both sigma2 and sigma2betaW acan be fixed or having an inverse-gamma prior and posterior distribution. Therefore, if the inverse gamma distribution(s) are used, the parameters in the distribution will be first updated and then samples of sigma2 and sigma2betaW will be generated using the updated parameters.

Examples


# create some data from the class 'Data'
myData <- Data(x=c(0.1,0.5,1.5,3,6,10,10,10),
               y=c(0,0,0,0,0,0,1,0),
               doseGrid=c(0.1,0.5,1.5,3,6,
                          seq(from=10,to=80,by=2)))

# Initialize the CRM model 
model <- LogisticLogNormal(mean=c(-0.85, 1),
                           cov=
                             matrix(c(1, -0.5, -0.5, 1),
                                    nrow=2),
                           refDose=56)


# Sample from the posterior distribution
options <- McmcOptions(burnin=100,
                       step=2,
                       samples=1000)

samples <- mcmc(data = myData, model = model, options=options)


##obtain mcmc DLE samples given the data, LogisticIndepBeta (DLE model) and mcmc simulations options
## data must be of 'Data' class
data<-Data(x=c(25,50,50,75,100,100,225,300),y=c(0,0,0,0,1,1,1,1),
           doseGrid=seq(25,300,25))
## model must be of 'LogisticIndepBeta' class
model<-LogisticIndepBeta(binDLE=c(1.05,1.8),DLEweights=c(3,3),DLEdose=c(25,300),data=data)
## options must be ''McmcOptions' class
options<-McmcOptions(burnin=100,step=2,samples=200)
set.seed(94)
samples<-mcmc(data=data,model=model,options=options)
##obtain mcmc efficacy samples given the data, 'Effloglog' model (efficacy model) and
## mcmc simulations options data must be of 'DataDual' class
data<-DataDual(x=c(25,50,25,50,75,300,250,150),
              y=c(0,0,0,0,0,1,1,0),
              w=c(0.31,0.42,0.59,0.45,0.6,0.7,0.6,0.52),
              doseGrid=seq(25,300,25),placebo=FALSE)
## model must be of 'Effloglog' class
Effmodel<-Effloglog(Eff=c(1.223,2.513),Effdose=c(25,300),nu=c(a=1,b=0.025),data=data,c=0)

## options must be ''McmcOptions' class
options<-McmcOptions(burnin=100,step=2,samples=200)
set.seed(94)
samples<-mcmc(data=data,model=Effmodel,options=options)
##obtain mcmc efficacy samples given the data, 'EffFlexi' model (efficacy model) and 
## mcmc simulations options
## data must be of 'DataDual' class
data<-DataDual(x=c(25,50,25,50,75,300,250,150),
               y=c(0,0,0,0,0,1,1,0),
               w=c(0.31,0.42,0.59,0.45,0.6,0.7,0.6,0.52),
               doseGrid=seq(25,300,25))
## model must be of 'EffFlexi' class

Effmodel<- EffFlexi(Eff=c(1.223, 2.513),Effdose=c(25,300),
                    sigma2=c(a=0.1,b=0.1),sigma2betaW=c(a=20,b=50),smooth="RW2",data=data)

## options must be ''McmcOptions' class
options<-McmcOptions(burnin=100,step=2,samples=200)
set.seed(94)
samples<-mcmc(data=data,model=Effmodel,options=options)
# create some data from the class 'Data'
myData <- Data(x=c(0.1,0.5,1.5,3,6,10,10,10),
               y=c(0,0,0,0,0,0,1,0),
               doseGrid=c(0.1,0.5,1.5,3,6,
                          seq(from=10,to=80,by=2)))

# Initialize the CRM model 
model <- LogisticLogNormal(mean=c(-0.85, 1),
                           cov=
                             matrix(c(1, -0.5, -0.5, 1),
                                    nrow=2),
                           refDose=56)


# Sample from the posterior distribution
options <- McmcOptions(burnin=100,
                       step=2,
                       samples=1000)

samples <- mcmc(data = myData, model = model, options=options)


##obtain mcmc DLE samples given the data, LogisticIndepBeta (DLE model) and mcmc simulations options
## data must be of 'Data' class
data<-Data(x=c(25,50,50,75,100,100,225,300),y=c(0,0,0,0,1,1,1,1),
           doseGrid=seq(25,300,25))
## model must be of 'LogisticIndepBeta' class
model<-LogisticIndepBeta(binDLE=c(1.05,1.8),DLEweights=c(3,3),DLEdose=c(25,300),data=data)
## options must be ''McmcOptions' class
options<-McmcOptions(burnin=100,step=2,samples=200)
set.seed(94)
samples<-mcmc(data=data,model=model,options=options)
##obtain mcmc efficacy samples given the data, 'Effloglog' model (efficacy model) and
## mcmc simulations options data must be of 'DataDual' class
data<-DataDual(x=c(25,50,25,50,75,300,250,150),
              y=c(0,0,0,0,0,1,1,0),
              w=c(0.31,0.42,0.59,0.45,0.6,0.7,0.6,0.52),
              doseGrid=seq(25,300,25),placebo=FALSE)
## model must be of 'Effloglog' class
Effmodel<-Effloglog(Eff=c(1.223,2.513),Effdose=c(25,300),nu=c(a=1,b=0.025),data=data,c=0)

## options must be ''McmcOptions' class
options<-McmcOptions(burnin=100,step=2,samples=200)
set.seed(94)
samples<-mcmc(data=data,model=Effmodel,options=options)
##obtain mcmc efficacy samples given the data, 'EffFlexi' model (efficacy model) and 
## mcmc simulations options
## data must be of 'DataDual' class
data<-DataDual(x=c(25,50,25,50,75,300,250,150),
               y=c(0,0,0,0,0,1,1,0),
               w=c(0.31,0.42,0.59,0.45,0.6,0.7,0.6,0.52),
               doseGrid=seq(25,300,25))
## model must be of 'EffFlexi' class

Effmodel<- EffFlexi(Eff=c(1.223, 2.513),Effdose=c(25,300),
                    sigma2=c(a=0.1,b=0.1),sigma2betaW=c(a=20,b=50),smooth="RW2",data=data)

## options must be ''McmcOptions' class
options<-McmcOptions(burnin=100,step=2,samples=200)
set.seed(94)
samples<-mcmc(data=data,model=Effmodel,options=options)

Initialization function for the "McmcOptions" class

Description

Initialization function for the "McmcOptions" class

Usage

McmcOptions(burnin = 10000L, step = 2L, samples = 10000L)
McmcOptions(burnin = 10000L, step = 2L, samples = 10000L)

Arguments

`burnin`	number of burn-in iterations which are not saved (default: `10,000`)
`step`	only every step-th iteration is saved after the burn-in (default: `2`)
`samples`	number of resulting samples (by default `10,000` will result)

Value

the McmcOptions object

Class for the three canonical MCMC options

Description

Class for the three canonical MCMC options

Slots

iterations: number of MCMC iterations
burnin: number of burn-in iterations which are not saved
step: only every step-th iteration is saved after the burn-in

Examples


# Set up MCMC option in order to have a burn-in of 10000 iterations and
# then take every other iteration up to a collection of 10000 samples
options <- McmcOptions(burnin=10000,
                       step=2,
                       samples=10000)

# Set up MCMC option in order to have a burn-in of 10000 iterations and
# then take every other iteration up to a collection of 10000 samples
options <- McmcOptions(burnin=10000,
                       step=2,
                       samples=10000)

Construct a minimally informative prior

Description

This function constructs a minimally informative prior, which is captured in a LogisticNormal (or LogisticLogNormal) object.

Usage

MinimalInformative(
  dosegrid,
  refDose,
  threshmin = 0.2,
  threshmax = 0.3,
  probmin = 0.05,
  probmax = 0.05,
  ...
)
MinimalInformative(
  dosegrid,
  refDose,
  threshmin = 0.2,
  threshmax = 0.3,
  probmin = 0.05,
  probmax = 0.05,
  ...
)

Arguments

`dosegrid`	the dose grid
`refDose`	the reference dose
`threshmin`	Any toxicity probability above this threshold would be very unlikely (see `probmin`) at the minimum dose (default: 0.2)
`threshmax`	Any toxicity probability below this threshold would be very unlikely (see `probmax`) at the maximum dose (default: 0.3)
`probmin`	the prior probability of exceeding `threshmin` at the minimum dose (default: 0.05)
`probmax`	the prior probability of being below `threshmax` at the maximum dose (default: 0.05)
`...`	additional arguments for computations, see `Quantiles2LogisticNormal`, e.g. `refDose` and `logNormal=TRUE` to obtain a minimal informative log normal prior.

Details

Based on the proposal by Neuenschwander et al (2008, Statistics in Medicine), a minimally informative prior distribution is constructed. The required key input is the minimum ( $d_{1}$ in the notation of the Appendix A.1 of that paper) and the maximum value ( $d_{J}$ ) of the dose grid supplied to this function. Then threshmin is the probability threshold $q_{1}$ , such that any probability of DLT larger than $q_{1}$ has only 5% probability. Therefore $q_{1}$ is the 95% quantile of the beta distribution and hence $p_{1} = 0.95$ . Likewise, threshmax is the probability threshold $q_{J}$ , such that any probability of DLT smaller than $q_{J}$ has only 5% probability ( $p_{J} = 0.05$ ). The probabilities $1 - p_{1}$ and $p_{J}$ can be controlled with the arguments probmin and probmax, respectively. Subsequently, for all doses supplied in the dosegrid argument, beta distributions are set up from the assumption that the prior medians are linear in log-dose on the logit scale, and Quantiles2LogisticNormal is used to transform the resulting quantiles into an approximating LogisticNormal (or LogisticLogNormal) model. Note that the reference dose is not required for these computations.

Value

see Quantiles2LogisticNormal

Examples


# Setting up a minimal informative prior 
# max.time is quite small only for the purpose of showing the example. They 
# should be increased for a real case.
set.seed(132)
coarseGrid <- c(0.1, 10, 30, 60, 100)
minInfModel <- MinimalInformative(dosegrid = coarseGrid,
                                  refDose=50,
                                  threshmin=0.2,
                                  threshmax=0.3,
                                  control=## for real case: leave out control 
                                    list(max.time=0.1)) 

# Plotting the result
matplot(x=coarseGrid,
        y=minInfModel$required,
        type="b", pch=19, col="blue", lty=1,
        xlab="dose",
        ylab="prior probability of DLT")
matlines(x=coarseGrid,
         y=minInfModel$quantiles,
         type="b", pch=19, col="red", lty=1)
legend("right",
       legend=c("quantiles", "approximation"),
       col=c("blue", "red"),
       lty=1,
       bty="n")

# Setting up a minimal informative prior 
# max.time is quite small only for the purpose of showing the example. They 
# should be increased for a real case.
set.seed(132)
coarseGrid <- c(0.1, 10, 30, 60, 100)
minInfModel <- MinimalInformative(dosegrid = coarseGrid,
                                  refDose=50,
                                  threshmin=0.2,
                                  threshmax=0.3,
                                  control=## for real case: leave out control 
                                    list(max.time=0.1)) 

# Plotting the result
matplot(x=coarseGrid,
        y=minInfModel$required,
        type="b", pch=19, col="blue", lty=1,
        xlab="dose",
        ylab="prior probability of DLT")
matlines(x=coarseGrid,
         y=minInfModel$quantiles,
         type="b", pch=19, col="red", lty=1)
legend("right",
       legend=c("quantiles", "approximation"),
       col=c("blue", "red"),
       lty=1,
       bty="n")

"MIN" combination of cohort size rules

Description

This function combines cohort size rules by taking the minimum of all sizes.

Usage

minSize(...)

## S4 method for signature 'CohortSize'
minSize(...)
minSize(...)

## S4 method for signature 'CohortSize'
minSize(...)

Arguments

...

Objects of class CohortSize

Value

the combination as an object of class CohortSizeMin

Functions

minSize(CohortSize): The method combining cohort size rules by taking minimum

Examples


# Here is the rule for: 
#   having cohort of size 1 for doses <30
#   and having cohort of size 3 for doses >=30
mySize1 <- CohortSizeRange(intervals = c(0, 30),
                           cohortSize = c(1, 3))

# Here is the rule for: 
#   having cohort of size 1 until no DLT were observed
#   and having cohort of size 3 as soon as 1 DLT is observed
mySize2 <- CohortSizeDLT(DLTintervals=c(0, 1),
                         cohortSize=c(1, 3))

# This is combining the two rules above by taking the minimum of the sample sizes of
# the single rules
mySize <- minSize(mySize1, mySize2)

# Here is the rule for: 
#   having cohort of size 1 for doses <30
#   and having cohort of size 3 for doses >=30
mySize1 <- CohortSizeRange(intervals = c(0, 30),
                           cohortSize = c(1, 3))

# Here is the rule for: 
#   having cohort of size 1 until no DLT were observed
#   and having cohort of size 3 as soon as 1 DLT is observed
mySize2 <- CohortSizeDLT(DLTintervals=c(0, 1),
                         cohortSize=c(1, 3))

# This is combining the two rules above by taking the minimum of the sample sizes of
# the single rules
mySize <- minSize(mySize1, mySize2)

Class for the model input

Description

This is the model class for single agent dose escalation, from which all other specific models inherit. It inherits all slots from GeneralModel.

Details

The datamodel must obey the convention that the data input is called exactly as in the Data class. All prior distributions for parameters should be contained in the model function priormodel. The background is that this can be used to simulate from the prior distribution, before obtaining any data.

The dose function has as first argument prob, a scalar toxicity probability which is targeted. Additional arguments are model parameters. Then it computes, using model parameter(s) (samples), the resulting dose. Note that the model parameters are called exactly as in the model and must be included in the sample vector. The vectors of all samples for these parameters will then be supplied to the function. So your function must be able to process vectors of the model parameters, i.e. it must vectorize over them.

The prob function has as first argument dose, which is a scalar dose. Additional arguments are model parameters. Then it computes, using model parameter(s) (samples), the resulting probability of toxicity at that dose. Again here, the function must vectorize over the model parameters.

If you work with multivariate parameters, then please assume that your the two functions receive either one parameter value as a row vector, or a samples matrix where the rows correspond to the sampling index, i.e. the layout is then nSamples x dimParameter.

Note that dose and prob are the inverse functions of each other.

Slots

dose: a function computing the dose reaching a specific target probability, based on the model parameters and additional prior settings (see the details above)
prob: a function computing the probability of toxicity for a specific dose, based on the model parameters and additional prior settings (see the details above)

No Initialization function class for Efficacy models using pseudo data prior

Description

This is a class of which contains all efficacy models for which their prior are specified in form of pseudo data. It inherits all slots from ModelPseudo

Details

The dose function has a first argument ExpEff, a scalar expected efficacy value which is targeted. Additional arguements are model parameters. It computes using modal estimate(s) or samples model parameter(s), the resulting expected efficacy value at that dose level. If samples of the model parameters are used, the function must vectorize over the model parameters.

The ExpEff function has a first argument dose, a scalar dose level which is targeted. Additional arguments are model parameters. It computes using modal estimates or samples of the model parameter(s), the resulting dose level given that particular expected efficacy value. If samples of the model parameter(s) are used, the function must vectorize over the model parameters.

The data must obey the covention that the data input is called exactly in the DataDual class. This refers to any observed Efficacy/biomarker responses (w in DataDual class), the dose (levels) (x in DataDual or Data class) at which these responses are observed, all dose levels considered in the study (doseGrid in DataDual or Data) class and other specifications in DataDual class that can be used to generate prior or posterior modal estimates or samples estimates for model parmater(s). If no responses is observed, at least doseGrid in DataDual has to be specified in data slot for which prior modal estimates or samples can be obtained for model parameters based on the specified pseudo data.

Slots

dose: a function computing the dose reaching a specific target value of expected efficacy, based on the model parameter(s). The model parameter(s) (samples) are obtained based on prior specified in form of pseudo data and if any together with any observed responses (see details above)
ExpEff: a function computing the expected efficacy (value) for a specific dose, based on model parameter(s). The model parameter(s) (samples) are obtained based on pseudo data prior and (if any) with observed responses (see details above)
data: refers to the data input specification in DataDual class which are used to obtain model paramters estimates or samples (see details above)

Class of models using expressing their prior in form of Pseudo data

Description

This is the Pseudo model class, from which all models where their prior are expressed in form of pseudo data (as if some data are available before the trial starts) inherit. It also inherits all slots from AllModels.No slots for this class

No intialization function Class for DLE models using pseudo data prior. This is a class of DLE (dose-limiting events) models/ toxicity model which contains all DLE models for which their prior are specified in form of pseudo data (as if there is some data before the trial starts). It inherits all slots from `ModelPseudo`

Description

The dose function has a first argument prob, a scalar a probability of the occurrence of a DLE which is targeted. Additional arguments are models parameters. It computes, using the model parameter(s)/ model parameter(s) samples, the resulting dose. Note that the model parameters are called exactly as in the model. The model estimates generated can be single values of the maximum likelihodd estimates (prior or posterior modal estimates) or samples of the model estimates generated. If samples of the model estimates are generated, the model parameters (samples) must be included in the samples vector. The vectors of all samples for these model paramters will be supplied to the function such that the function will be able to process vectors of model parameters.

Details

The prob function has a first argument dose, a scalar dose level which is targeted. Additional arguments are model paramters. It computes using model paramter(s) (samples), the resulting probabilities of a DLE occuring at the target dose level. If samples of model parameters are generated, the function must vectorize over the model parameters.

Note that dose and prob are the inverse functions of each other.

The data must obey the covention that the data input is called exactly in the Data class. This refers to any observed DLE responses (y in Data class), the dose (levels) (x in Data class) at which these responses are observed, all dose levels considered in the study (doseGrid in Data) class and other specifications in Data class that can be used to generate prior or posterior modal estimates or samples estimates for model parmater(s). If no responses is observed, at least doseGrid in Data has to be specified in data slot for which prior modal estimates or samples can be obtained for model parameters based on the specified pseudo data.

Slots

dose: a function computing the dose level reaching a specific target probabilty of the occurrence of a DLE, based on the model parameters. The model paramters (samples)are obtained based on the prior specified in form of pseudo data and together with (if any) the observed DLE responses and their corresponding dose levels (see details above)
prob: a function computing the probability of the occurrence of a DLEat a specidfied dose level, based on the model parameters. The model paramters (samples) are obtained the prior specified in form of pseudo data and together with (if any) the observed DLE responses and their corresponding dose levels (see dtails above)
data: refers to the data input specification in Data class which are used to obtain model paramters estimates or samples (see details above)

Multiple plot function

Description

ggplot objects can be passed in ..., or to plotlist (as a list of ggplot objects). If the layout is something like matrix(c(1,2,3,3), nrow=2, byrow=TRUE), then plot 1 will go in the upper left, 2 will go in the upper right, and 3 will go all the way across the bottom.

Usage

multiplot(..., plotlist = NULL, rows = 1, layout = NULL)
multiplot(..., plotlist = NULL, rows = 1, layout = NULL)

Arguments

`...`	Objects to be passed
`plotlist`	a list of additional objects
`rows`	Number of rows in layout
`layout`	A matrix specifying the layout. If present, `rows` is ignored.

Value

Used for the side effect of plotting

Find the next best dose

Description

Compute the recommended next best dose.

Usage

nextBest(nextBest, doselimit, samples, model, data, ...)

## S4 method for signature 'NextBestMTD,numeric,Samples,Model,Data'
nextBest(nextBest, doselimit, samples, model, data, ...)

## S4 method for signature 'NextBestNCRM,numeric,Samples,Model,Data'
nextBest(nextBest, doselimit, samples, model, data, ...)

## S4 method for signature 'NextBestNCRM,numeric,Samples,Model,DataParts'
nextBest(nextBest, doselimit, samples, model, data, ...)

## S4 method for signature 
## 'NextBestThreePlusThree,missing,missing,missing,Data'
nextBest(nextBest, doselimit, samples, model, data, ...)

## S4 method for signature 
## 'NextBestDualEndpoint,numeric,Samples,DualEndpoint,Data'
nextBest(nextBest, doselimit, samples, model, data, ...)

## S4 method for signature 
## 'NextBestTDsamples,numeric,Samples,LogisticIndepBeta,Data'
nextBest(nextBest, doselimit, samples, model, data, ...)

## S4 method for signature 'NextBestTD,numeric,missing,LogisticIndepBeta,Data'
nextBest(nextBest, doselimit, model, data, SIM = FALSE, ...)

## S4 method for signature 'NextBestMaxGain,numeric,missing,ModelTox,DataDual'
nextBest(nextBest, doselimit, model, data, Effmodel, SIM = FALSE, ...)

## S4 method for signature 
## 'NextBestMaxGainSamples,numeric,Samples,ModelTox,DataDual'
nextBest(
  nextBest,
  doselimit,
  samples,
  model,
  data,
  Effmodel,
  Effsamples,
  SIM = FALSE,
  ...
)
nextBest(nextBest, doselimit, samples, model, data, ...)

## S4 method for signature 'NextBestMTD,numeric,Samples,Model,Data'
nextBest(nextBest, doselimit, samples, model, data, ...)

## S4 method for signature 'NextBestNCRM,numeric,Samples,Model,Data'
nextBest(nextBest, doselimit, samples, model, data, ...)

## S4 method for signature 'NextBestNCRM,numeric,Samples,Model,DataParts'
nextBest(nextBest, doselimit, samples, model, data, ...)

## S4 method for signature 
## 'NextBestThreePlusThree,missing,missing,missing,Data'
nextBest(nextBest, doselimit, samples, model, data, ...)

## S4 method for signature 
## 'NextBestDualEndpoint,numeric,Samples,DualEndpoint,Data'
nextBest(nextBest, doselimit, samples, model, data, ...)

## S4 method for signature 
## 'NextBestTDsamples,numeric,Samples,LogisticIndepBeta,Data'
nextBest(nextBest, doselimit, samples, model, data, ...)

## S4 method for signature 'NextBestTD,numeric,missing,LogisticIndepBeta,Data'
nextBest(nextBest, doselimit, model, data, SIM = FALSE, ...)

## S4 method for signature 'NextBestMaxGain,numeric,missing,ModelTox,DataDual'
nextBest(nextBest, doselimit, model, data, Effmodel, SIM = FALSE, ...)

## S4 method for signature 
## 'NextBestMaxGainSamples,numeric,Samples,ModelTox,DataDual'
nextBest(
  nextBest,
  doselimit,
  samples,
  model,
  data,
  Effmodel,
  Effsamples,
  SIM = FALSE,
  ...
)

Arguments

`nextBest`	The rule, an object of class `NextBest`
`doselimit`	The maximum allowed next dose. If this is an empty (length 0) vector, then no dose limit will be applied in the course of dose recommendation calculation, and a corresponding warning is given.
`samples`	the `Samples` object
`model`	The model input, an object of class `Model`
`data`	The data input, an object of class `Data`
`...`	possible additional arguments without method dispatch
`SIM`	internal command to notify if this method is used within simulations. Default as FALSE
`Effmodel`	the efficacy model of `ModelEff` class object
`Effsamples`	the efficacy samples of `Samples` class object

Details

This function outputs the next best dose recommendation based on the corresponding rule nextBest, the posterior samples from the model and the underlying data.

Value

a list with the next best dose (element value) on the grid defined in data, and a plot depicting this recommendation (element plot). In case of multiple plots also an element singlePlots is included which returns the list of single plots, which allows for further customization of these. Also additional list elements describing the outcome of the rule can be contained.

Functions

nextBest( nextBest = NextBestMTD, doselimit = numeric, samples = Samples, model = Model, data = Data ): Find the next best dose based on the MTD rule
nextBest( nextBest = NextBestNCRM, doselimit = numeric, samples = Samples, model = Model, data = Data ): Find the next best dose based on the NCRM method. The additional list element probs contains the target and overdosing probabilities (across all doses in the dose grid) used in the derivation of the next best dose.
nextBest( nextBest = NextBestNCRM, doselimit = numeric, samples = Samples, model = Model, data = DataParts ): Find the next best dose based on the NCRM method when two parts trial is used - todo: need an example here for DataParts
nextBest( nextBest = NextBestThreePlusThree, doselimit = missing, samples = missing, model = missing, data = Data ): Find the next best dose based on the 3+3 method
nextBest( nextBest = NextBestDualEndpoint, doselimit = numeric, samples = Samples, model = DualEndpoint, data = Data ): Find the next best dose based on the dual endpoint model. The additional list element probs contains the target and overdosing probabilities (across all doses in the dose grid) used in the derivation of the next best dose.
nextBest( nextBest = NextBestTDsamples, doselimit = numeric, samples = Samples, model = LogisticIndepBeta, data = Data ): Find the next best dose based on the 'NextBestTDsamples' class rule. This a method based only on the DLE responses and for LogisticIndepBeta model class object involving DLE samples
nextBest( nextBest = NextBestTD, doselimit = numeric, samples = missing, model = LogisticIndepBeta, data = Data ): Find the next best dose based on the 'NextBestTD' class rule. This a method based only on the DLE responses and for LogisticIndepBeta model class object without DLE samples
nextBest( nextBest = NextBestMaxGain, doselimit = numeric, samples = missing, model = ModelTox, data = DataDual ): for slots nextBest,doselimit, data and SIM. This is a function to find the next best dose based on the 'NextBestMaxGain' class rule. This a method based on the DLE responses and efficacy responses without DLE and efficacy samples.
nextBest( nextBest = NextBestMaxGainSamples, doselimit = numeric, samples = Samples, model = ModelTox, data = DataDual ): for slots nextBest,doselimit, data and SIM. This is a function to find the next best dose based on the 'NextBestMaxGainSamples' class rule. This a method based on the DLE responses and efficacy responses with DLE and efficacy samples. Effmodel must be of class Effloglog or EffFlexi.

Examples


# Create the data
data <- Data(x=c(0.1, 0.5, 1.5, 3, 6, 10, 10, 10),
             y=c(0, 0, 0, 0, 0, 0, 1, 0),
             cohort=c(0, 1, 2, 3, 4, 5, 5, 5),
             doseGrid=
               c(0.1, 0.5, 1.5, 3, 6,
                 seq(from=10, to=80, by=2)))

# Initialize the CRM model used to model the data
model <- LogisticLogNormal(mean=c(-0.85, 1),
                           cov=
                             matrix(c(1, -0.5, -0.5, 1),
                                    nrow=2),
                           refDose=56)

# Set-up some MCMC parameters and generate samples from the posterior
options <- McmcOptions(burnin=100,
                       step=2,
                       samples=2000)
set.seed(94)
samples <- mcmc(data, model, options)

# Define the rule for dose increments and calculate the maximum dose allowed
myIncrements <- IncrementsRelative(intervals=c(0, 20),
                                   increments=c(1, 0.33))
nextMaxDose <- maxDose(myIncrements,
                       data=data)

# Define the rule which will be used to select the next best dose
# based on the class 'NextBestMTD'
mtdNextBest <- NextBestMTD(target=0.33,
                           derive=
                             function(mtdSamples){
                               quantile(mtdSamples, probs=0.25)
                             })

# Calculate the next best dose
doseRecommendation <- nextBest(mtdNextBest,
                               doselimit=nextMaxDose,
                               samples=samples, model=model, data=data)

# Create the data
data <- Data(x=c(0.1, 0.5, 1.5, 3, 6, 10, 10, 10),
             y=c(0, 0, 0, 0, 0, 0, 1, 0),
             cohort=c(0, 1, 2, 3, 4, 5, 5, 5),
             doseGrid=
               c(0.1, 0.5, 1.5, 3, 6,
                 seq(from=10, to=80, by=2)))

# Initialize the CRM model used to model the data
model <- LogisticLogNormal(mean=c(-0.85, 1),
                           cov=
                             matrix(c(1, -0.5, -0.5, 1),
                                    nrow=2),
                           refDose=56)

# Set-up some MCMC parameters and generate samples from the posterior
options <- McmcOptions(burnin=100,
                       step=2,
                       samples=2000)
set.seed(94)
samples <- mcmc(data, model, options)

# Define the rule for dose increments and calculate the maximum dose allowed
myIncrements <- IncrementsRelative(intervals=c(0, 20),
                                   increments=c(1, 0.33))
nextMaxDose <- maxDose(myIncrements,
                       data=data)

# Define the rule which will be used to select the next best dose
# based on the class 'NextBestNCRM'
myNextBest <- NextBestNCRM(target=c(0.2, 0.35),
                           overdose=c(0.35, 1),
                           maxOverdoseProb=0.25)

# Calculate the next best dose
doseRecommendation <- nextBest(myNextBest,
                               doselimit=nextMaxDose,
                               samples=samples, model=model, data=data)

# Look at the probabilities
doseRecommendation$probs



# create an object of class 'DataParts'
data <- DataParts(x=c(0.1,0.5,1.5),
                    y=c(0,0,0),
                    doseGrid=c(0.1,0.5,1.5,3,6,
                               seq(from=10,to=80,by=2)),
                    part=c(1L,1L,1L),
                    nextPart=1L,
                    part1Ladder=c(0.1,0.5,1.5,3,6,10))

# Initialize the CRM model used to model the data
model <- LogisticLogNormal(mean=c(-0.85, 1),
                           cov=
                             matrix(c(1, -0.5, -0.5, 1),
                                    nrow=2),
                           refDose=56)

# Set-up some MCMC parameters and generate samples from the posterior
options <- McmcOptions(burnin=100,
                       step=2,
                       samples=2000)
set.seed(94)
samples <- mcmc(data, model, options)

myIncrements <- IncrementsRelativeParts(dltStart=0,
                                        cleanStart=1)
nextMaxDose <- maxDose(myIncrements,
                       data=data)

# Define the rule which will be used to select the next best dose
# based on the class 'NextBestNCRM'
myNextBest <- NextBestNCRM(target=c(0.2, 0.35),
                           overdose=c(0.35, 1),
                           maxOverdoseProb=0.25)

# Calculate the next best dose
doseRecommendation <- nextBest(myNextBest,
                               doselimit=nextMaxDose,
                               samples=samples, 
                               model=model, 
                               data=data)


# Create the data
data <- Data(x=c(5, 5, 5, 10, 10, 10),
             y=c(0, 0, 0, 0, 1, 0),
             cohort=c(0, 0, 0, 1, 1, 1),
             doseGrid=
               c(0.1, 0.5, 1.5, 3, 5,
                 seq(from=10, to=80, by=2)))


# The rule to select the next best dose will be based on the 3+3 method
myNextBest <- NextBestThreePlusThree()

# Calculate the next best dose
doseRecommendation <- nextBest(myNextBest,
                               data=data)


# Create the data
data <- DataDual(
  x=c(0.1, 0.5, 1.5, 3, 6, 10, 10, 10,
      20, 20, 20, 40, 40, 40, 50, 50, 50),
  y=c(0, 0, 0, 0, 0, 0, 1, 0,
      0, 1, 1, 0, 0, 1, 0, 1, 1),
  w=c(0.31, 0.42, 0.59, 0.45, 0.6, 0.7, 0.55, 0.6,
      0.52, 0.54, 0.56, 0.43, 0.41, 0.39, 0.34, 0.38, 0.21),
  doseGrid=c(0.1, 0.5, 1.5, 3, 6,
             seq(from=10, to=80, by=2)))

# Initialize the Dual-Endpoint model (in this case RW1)
model <- DualEndpointRW(mu = c(0, 1),
                        Sigma = matrix(c(1, 0, 0, 1), nrow=2),
                        sigma2betaW = 0.01,
                        sigma2W = c(a=0.1, b=0.1),
                        rho = c(a=1, b=1),
                        smooth = "RW1")

# Set-up some MCMC parameters and generate samples from the posterior
options <- McmcOptions(burnin=100,
                       step=2,
                       samples=500)
set.seed(94)
samples <- mcmc(data, model, options)

# Define the rule for dose increments and calculate the maximum dose allowed
myIncrements <- IncrementsRelative(intervals=c(0, 20),
                                   increments=c(1, 0.33))
nextMaxDose <- maxDose(myIncrements,
                       data=data)

# Define the rule which will be used to select the next best dose
# In this case target a dose achieving at least 0.9 of maximum biomarker level (efficacy)
# and with a probability below 0.25 that prob(DLT)>0.35 (safety)
myNextBest <- NextBestDualEndpoint(target=c(0.9, 1),
                                   overdose=c(0.35, 1),
                                   maxOverdoseProb=0.25)

# Calculate the next best dose
doseRecommendation <- nextBest(myNextBest,
                               doselimit=nextMaxDose,
                               samples=samples,
                               model=model,
                               data=data)

## joint plot
print(doseRecommendation$plot)

## show customization of single plot
variant1 <- doseRecommendation$singlePlots$plot1 + xlim(0, 20)
print(variant1)

## we need a data object with doses >= 1:
data<-Data(x=c(25,50,50,75,150,200,225,300),
           y=c(0,0,0,0,1,1,1,1),
           doseGrid=seq(from=25,to=300,by=25))
##The 'nextBest' method using NextBestTDsamples' rules class object
## That is dose-esclation procedure using the 'logisticIndepBeta' DLE model involving DLE samples
## model must be of 'LogisticIndepBeta' class
model<-LogisticIndepBeta(binDLE=c(1.05,1.8),DLEweights=c(3,3),DLEdose=c(25,300),data=data)

##Define the options for MCMC
options <- McmcOptions(burnin=100,step=2,samples=1000)
##Then genreate the samples
samples <- mcmc(data, model, options)

##target probabilities of the occurrence of a DLE during trial and at the end of trial are 
## defined as 0.35 and 0.3, respectively
##Specified in 'derive' such that the 30% posterior quantile of the TD35 and TD30 samples 
## will be used as TD35 and TD30 estimates
tdNextBest<-NextBestTDsamples(targetDuringTrial=0.35,targetEndOfTrial=0.3,
                              derive=function(TDsamples){quantile(TDsamples,probs=0.3)})

##doselimit is the maximum allowable dose level to be given to subjects
RecommendDose<-nextBest(tdNextBest,doselimit=max(data@doseGrid),samples=samples,
                        model=model,data=data)
## we need a data object with doses >= 1:
data<-Data(x=c(25,50,50,75,150,200,225,300),
           y=c(0,0,0,0,1,1,1,1),
           doseGrid=seq(from=25,to=300,by=25))
##The 'nextBest' method using NextBestTD' rules class object
## That is dose-esclation procedure using the 'logisticIndepBeta' DLE model involving DLE samples
## model must be of 'LogisticIndepBeta' class
model<-LogisticIndepBeta(binDLE=c(1.05,1.8),DLEweights=c(3,3),DLEdose=c(25,300),data=data)
##target probabilities of the occurrence of a DLE during trial and at the end of trial 
## are defined as 0.35 and 0.3, respectively
tdNextBest<-NextBestTD(targetDuringTrial=0.35,targetEndOfTrial=0.3)

##doselimit is the maximum allowable dose level to be given to subjects
RecommendDose<- nextBest(tdNextBest,
              doselimit=max(data@doseGrid),
              model=model,
              data=data)
## we need a data object with doses >= 1:
data <-DataDual(x=c(25,50,25,50,75,300,250,150),
               y=c(0,0,0,0,0,1,1,0),
               w=c(0.31,0.42,0.59,0.45,0.6,0.7,0.6,0.52),
               doseGrid=seq(25,300,25),placebo=FALSE)

##The 'nextBest' method using NextBestMaxGain' rules class object
## using the 'ModelTox' class DLE model 
## DLEmodel e.g 'LogisticIndepBeta' class
DLEmodel<-LogisticIndepBeta(binDLE=c(1.05,1.8),DLEweights=c(3,3),DLEdose=c(25,300),data=data)

## using the 'ModelEff' class efficacy model 
## Effmodel e.g 'Effloglog' class
Effmodel<-Effloglog(Eff=c(1.223,2.513),Effdose=c(25,300),nu=c(a=1,b=0.025),data=data,c=0)

##target probabilities of the occurrence of a DLE during trial and at the
## end of trial are defined as
## 0.35 and 0.3, respectively
mynextbest<-NextBestMaxGain(DLEDuringTrialtarget=0.35,DLEEndOfTrialtarget=0.3)

##doselimit is the maximum allowable dose level to be given to subjects
RecommendDose<-nextBest(mynextbest,doselimit=300,model=DLEmodel,Effmodel=Effmodel,data=data)
data <-DataDual(x=c(25,50,25,50,75,300,250,150),
                y=c(0,0,0,0,0,1,1,0),
                w=c(0.31,0.42,0.59,0.45,0.6,0.7,0.6,0.52),
                doseGrid=seq(25,300,25),placebo=FALSE)
##The 'nextBest' method using NextBestMaxGainSamples' rules class object
## using the 'ModelTox' class DLE model 
## DLEmodel e.g 'LogisticIndepBeta' class
DLEmodel<-LogisticIndepBeta(binDLE=c(1.05,1.8),DLEweights=c(3,3),DLEdose=c(25,300),data=data)

## using the 'ModelEff' class efficacy model 
## Effmodel e.g 'Effloglog' class
Effmodel<-Effloglog(c(1.223,2.513),c(25,300),nu=c(a=1,b=0.025),data=data,c=0)
##DLE and efficacy samples must be of 'Samples' Class
DLEsamples<-mcmc(data,DLEmodel,options)
Effsamples<-mcmc(data,Effmodel,options)

##target probabilities of the occurrence of a DLE during trial and at the end of trial 
## are defined as 0.35 and 0.3, respectively
## Using 30% posterior quantile of the TD35 and TD30 samples as estimates of TD35 and TD30, 
## function specified in TDderive slot
## Using the 50% posterior quantile of the Gstar (the dose which gives the maxim gain value) 
## samples as Gstar estimate,function specified in Gstarderive slot 
mynextbest<-NextBestMaxGainSamples(DLEDuringTrialtarget=0.35,
                                   DLEEndOfTrialtarget=0.3,
                                   TDderive=function(TDsamples){
                                     quantile(TDsamples,prob=0.3)},
                                   Gstarderive=function(Gstarsamples){
                                     quantile(Gstarsamples,prob=0.5)})

RecommendDose<-nextBest(mynextbest,doselimit=max(data@doseGrid),samples=DLEsamples,model=DLEmodel,
                        data=data,Effmodel=Effmodel,Effsamples=Effsamples)

## now using the 'EffFlexi' class efficacy model:

##The 'nextBest' method using NextBestMaxGainSamples' rules class object for 'EffFlexi' model class
## using the 'ModelTox' class DLE model 
## DLEmodel e.g 'LogisticIndepBeta' class
Effmodel<- EffFlexi(Eff=c(1.223, 2.513),Effdose=c(25,300),
                    sigma2=c(a=0.1,b=0.1),
                    sigma2betaW=c(a=20,b=50),smooth="RW2",data=data)

##DLE and efficacy samples must be of 'Samples' Class
DLEsamples<-mcmc(data,DLEmodel,options)
Effsamples<-mcmc(data,Effmodel,options)

##target probabilities of the occurrence of a DLE during trial and at the 
## end of trial are defined as 0.35 and 0.3, respectively
## Using 30% posterior quantile of the TD35 and TD30 samples as estimates of 
## TD35 and TD30, function specified in TDderive slot
## Using the 50% posterio quantile of the Gstar (the dose which gives the maximum 
## gain value) samples as Gstar estimate,function specified in Gstarderive slot 
mynextbest<-NextBestMaxGainSamples(DLEDuringTrialtarget=0.35,
                                   DLEEndOfTrialtarget=0.3,
                                   TDderive=function(TDsamples){
                                     quantile(TDsamples,prob=0.3)},
                                   Gstarderive=function(Gstarsamples){
                                     quantile(Gstarsamples,prob=0.5)})

RecommendDose<-nextBest(mynextbest,doselimit=max(data@doseGrid),samples=DLEsamples,
                        model=DLEmodel,
                        data=data,Effmodel=Effmodel,Effsamples=Effsamples)

# Create the data
data <- Data(x=c(0.1, 0.5, 1.5, 3, 6, 10, 10, 10),
             y=c(0, 0, 0, 0, 0, 0, 1, 0),
             cohort=c(0, 1, 2, 3, 4, 5, 5, 5),
             doseGrid=
               c(0.1, 0.5, 1.5, 3, 6,
                 seq(from=10, to=80, by=2)))

# Initialize the CRM model used to model the data
model <- LogisticLogNormal(mean=c(-0.85, 1),
                           cov=
                             matrix(c(1, -0.5, -0.5, 1),
                                    nrow=2),
                           refDose=56)

# Set-up some MCMC parameters and generate samples from the posterior
options <- McmcOptions(burnin=100,
                       step=2,
                       samples=2000)
set.seed(94)
samples <- mcmc(data, model, options)

# Define the rule for dose increments and calculate the maximum dose allowed
myIncrements <- IncrementsRelative(intervals=c(0, 20),
                                   increments=c(1, 0.33))
nextMaxDose <- maxDose(myIncrements,
                       data=data)

# Define the rule which will be used to select the next best dose
# based on the class 'NextBestMTD'
mtdNextBest <- NextBestMTD(target=0.33,
                           derive=
                             function(mtdSamples){
                               quantile(mtdSamples, probs=0.25)
                             })

# Calculate the next best dose
doseRecommendation <- nextBest(mtdNextBest,
                               doselimit=nextMaxDose,
                               samples=samples, model=model, data=data)

# Create the data
data <- Data(x=c(0.1, 0.5, 1.5, 3, 6, 10, 10, 10),
             y=c(0, 0, 0, 0, 0, 0, 1, 0),
             cohort=c(0, 1, 2, 3, 4, 5, 5, 5),
             doseGrid=
               c(0.1, 0.5, 1.5, 3, 6,
                 seq(from=10, to=80, by=2)))

# Initialize the CRM model used to model the data
model <- LogisticLogNormal(mean=c(-0.85, 1),
                           cov=
                             matrix(c(1, -0.5, -0.5, 1),
                                    nrow=2),
                           refDose=56)

# Set-up some MCMC parameters and generate samples from the posterior
options <- McmcOptions(burnin=100,
                       step=2,
                       samples=2000)
set.seed(94)
samples <- mcmc(data, model, options)

# Define the rule for dose increments and calculate the maximum dose allowed
myIncrements <- IncrementsRelative(intervals=c(0, 20),
                                   increments=c(1, 0.33))
nextMaxDose <- maxDose(myIncrements,
                       data=data)

# Define the rule which will be used to select the next best dose
# based on the class 'NextBestNCRM'
myNextBest <- NextBestNCRM(target=c(0.2, 0.35),
                           overdose=c(0.35, 1),
                           maxOverdoseProb=0.25)

# Calculate the next best dose
doseRecommendation <- nextBest(myNextBest,
                               doselimit=nextMaxDose,
                               samples=samples, model=model, data=data)

# Look at the probabilities
doseRecommendation$probs



# create an object of class 'DataParts'
data <- DataParts(x=c(0.1,0.5,1.5),
                    y=c(0,0,0),
                    doseGrid=c(0.1,0.5,1.5,3,6,
                               seq(from=10,to=80,by=2)),
                    part=c(1L,1L,1L),
                    nextPart=1L,
                    part1Ladder=c(0.1,0.5,1.5,3,6,10))

# Initialize the CRM model used to model the data
model <- LogisticLogNormal(mean=c(-0.85, 1),
                           cov=
                             matrix(c(1, -0.5, -0.5, 1),
                                    nrow=2),
                           refDose=56)

# Set-up some MCMC parameters and generate samples from the posterior
options <- McmcOptions(burnin=100,
                       step=2,
                       samples=2000)
set.seed(94)
samples <- mcmc(data, model, options)

myIncrements <- IncrementsRelativeParts(dltStart=0,
                                        cleanStart=1)
nextMaxDose <- maxDose(myIncrements,
                       data=data)

# Define the rule which will be used to select the next best dose
# based on the class 'NextBestNCRM'
myNextBest <- NextBestNCRM(target=c(0.2, 0.35),
                           overdose=c(0.35, 1),
                           maxOverdoseProb=0.25)

# Calculate the next best dose
doseRecommendation <- nextBest(myNextBest,
                               doselimit=nextMaxDose,
                               samples=samples, 
                               model=model, 
                               data=data)


# Create the data
data <- Data(x=c(5, 5, 5, 10, 10, 10),
             y=c(0, 0, 0, 0, 1, 0),
             cohort=c(0, 0, 0, 1, 1, 1),
             doseGrid=
               c(0.1, 0.5, 1.5, 3, 5,
                 seq(from=10, to=80, by=2)))


# The rule to select the next best dose will be based on the 3+3 method
myNextBest <- NextBestThreePlusThree()

# Calculate the next best dose
doseRecommendation <- nextBest(myNextBest,
                               data=data)


# Create the data
data <- DataDual(
  x=c(0.1, 0.5, 1.5, 3, 6, 10, 10, 10,
      20, 20, 20, 40, 40, 40, 50, 50, 50),
  y=c(0, 0, 0, 0, 0, 0, 1, 0,
      0, 1, 1, 0, 0, 1, 0, 1, 1),
  w=c(0.31, 0.42, 0.59, 0.45, 0.6, 0.7, 0.55, 0.6,
      0.52, 0.54, 0.56, 0.43, 0.41, 0.39, 0.34, 0.38, 0.21),
  doseGrid=c(0.1, 0.5, 1.5, 3, 6,
             seq(from=10, to=80, by=2)))

# Initialize the Dual-Endpoint model (in this case RW1)
model <- DualEndpointRW(mu = c(0, 1),
                        Sigma = matrix(c(1, 0, 0, 1), nrow=2),
                        sigma2betaW = 0.01,
                        sigma2W = c(a=0.1, b=0.1),
                        rho = c(a=1, b=1),
                        smooth = "RW1")

# Set-up some MCMC parameters and generate samples from the posterior
options <- McmcOptions(burnin=100,
                       step=2,
                       samples=500)
set.seed(94)
samples <- mcmc(data, model, options)

# Define the rule for dose increments and calculate the maximum dose allowed
myIncrements <- IncrementsRelative(intervals=c(0, 20),
                                   increments=c(1, 0.33))
nextMaxDose <- maxDose(myIncrements,
                       data=data)

# Define the rule which will be used to select the next best dose
# In this case target a dose achieving at least 0.9 of maximum biomarker level (efficacy)
# and with a probability below 0.25 that prob(DLT)>0.35 (safety)
myNextBest <- NextBestDualEndpoint(target=c(0.9, 1),
                                   overdose=c(0.35, 1),
                                   maxOverdoseProb=0.25)

# Calculate the next best dose
doseRecommendation <- nextBest(myNextBest,
                               doselimit=nextMaxDose,
                               samples=samples,
                               model=model,
                               data=data)

## joint plot
print(doseRecommendation$plot)

## show customization of single plot
variant1 <- doseRecommendation$singlePlots$plot1 + xlim(0, 20)
print(variant1)

## we need a data object with doses >= 1:
data<-Data(x=c(25,50,50,75,150,200,225,300),
           y=c(0,0,0,0,1,1,1,1),
           doseGrid=seq(from=25,to=300,by=25))
##The 'nextBest' method using NextBestTDsamples' rules class object
## That is dose-esclation procedure using the 'logisticIndepBeta' DLE model involving DLE samples
## model must be of 'LogisticIndepBeta' class
model<-LogisticIndepBeta(binDLE=c(1.05,1.8),DLEweights=c(3,3),DLEdose=c(25,300),data=data)

##Define the options for MCMC
options <- McmcOptions(burnin=100,step=2,samples=1000)
##Then genreate the samples
samples <- mcmc(data, model, options)

##target probabilities of the occurrence of a DLE during trial and at the end of trial are 
## defined as 0.35 and 0.3, respectively
##Specified in 'derive' such that the 30% posterior quantile of the TD35 and TD30 samples 
## will be used as TD35 and TD30 estimates
tdNextBest<-NextBestTDsamples(targetDuringTrial=0.35,targetEndOfTrial=0.3,
                              derive=function(TDsamples){quantile(TDsamples,probs=0.3)})

##doselimit is the maximum allowable dose level to be given to subjects
RecommendDose<-nextBest(tdNextBest,doselimit=max(data@doseGrid),samples=samples,
                        model=model,data=data)
## we need a data object with doses >= 1:
data<-Data(x=c(25,50,50,75,150,200,225,300),
           y=c(0,0,0,0,1,1,1,1),
           doseGrid=seq(from=25,to=300,by=25))
##The 'nextBest' method using NextBestTD' rules class object
## That is dose-esclation procedure using the 'logisticIndepBeta' DLE model involving DLE samples
## model must be of 'LogisticIndepBeta' class
model<-LogisticIndepBeta(binDLE=c(1.05,1.8),DLEweights=c(3,3),DLEdose=c(25,300),data=data)
##target probabilities of the occurrence of a DLE during trial and at the end of trial 
## are defined as 0.35 and 0.3, respectively
tdNextBest<-NextBestTD(targetDuringTrial=0.35,targetEndOfTrial=0.3)

##doselimit is the maximum allowable dose level to be given to subjects
RecommendDose<- nextBest(tdNextBest,
              doselimit=max(data@doseGrid),
              model=model,
              data=data)
## we need a data object with doses >= 1:
data <-DataDual(x=c(25,50,25,50,75,300,250,150),
               y=c(0,0,0,0,0,1,1,0),
               w=c(0.31,0.42,0.59,0.45,0.6,0.7,0.6,0.52),
               doseGrid=seq(25,300,25),placebo=FALSE)

##The 'nextBest' method using NextBestMaxGain' rules class object
## using the 'ModelTox' class DLE model 
## DLEmodel e.g 'LogisticIndepBeta' class
DLEmodel<-LogisticIndepBeta(binDLE=c(1.05,1.8),DLEweights=c(3,3),DLEdose=c(25,300),data=data)

## using the 'ModelEff' class efficacy model 
## Effmodel e.g 'Effloglog' class
Effmodel<-Effloglog(Eff=c(1.223,2.513),Effdose=c(25,300),nu=c(a=1,b=0.025),data=data,c=0)

##target probabilities of the occurrence of a DLE during trial and at the
## end of trial are defined as
## 0.35 and 0.3, respectively
mynextbest<-NextBestMaxGain(DLEDuringTrialtarget=0.35,DLEEndOfTrialtarget=0.3)

##doselimit is the maximum allowable dose level to be given to subjects
RecommendDose<-nextBest(mynextbest,doselimit=300,model=DLEmodel,Effmodel=Effmodel,data=data)
data <-DataDual(x=c(25,50,25,50,75,300,250,150),
                y=c(0,0,0,0,0,1,1,0),
                w=c(0.31,0.42,0.59,0.45,0.6,0.7,0.6,0.52),
                doseGrid=seq(25,300,25),placebo=FALSE)
##The 'nextBest' method using NextBestMaxGainSamples' rules class object
## using the 'ModelTox' class DLE model 
## DLEmodel e.g 'LogisticIndepBeta' class
DLEmodel<-LogisticIndepBeta(binDLE=c(1.05,1.8),DLEweights=c(3,3),DLEdose=c(25,300),data=data)

## using the 'ModelEff' class efficacy model 
## Effmodel e.g 'Effloglog' class
Effmodel<-Effloglog(c(1.223,2.513),c(25,300),nu=c(a=1,b=0.025),data=data,c=0)
##DLE and efficacy samples must be of 'Samples' Class
DLEsamples<-mcmc(data,DLEmodel,options)
Effsamples<-mcmc(data,Effmodel,options)

##target probabilities of the occurrence of a DLE during trial and at the end of trial 
## are defined as 0.35 and 0.3, respectively
## Using 30% posterior quantile of the TD35 and TD30 samples as estimates of TD35 and TD30, 
## function specified in TDderive slot
## Using the 50% posterior quantile of the Gstar (the dose which gives the maxim gain value) 
## samples as Gstar estimate,function specified in Gstarderive slot 
mynextbest<-NextBestMaxGainSamples(DLEDuringTrialtarget=0.35,
                                   DLEEndOfTrialtarget=0.3,
                                   TDderive=function(TDsamples){
                                     quantile(TDsamples,prob=0.3)},
                                   Gstarderive=function(Gstarsamples){
                                     quantile(Gstarsamples,prob=0.5)})

RecommendDose<-nextBest(mynextbest,doselimit=max(data@doseGrid),samples=DLEsamples,model=DLEmodel,
                        data=data,Effmodel=Effmodel,Effsamples=Effsamples)

## now using the 'EffFlexi' class efficacy model:

##The 'nextBest' method using NextBestMaxGainSamples' rules class object for 'EffFlexi' model class
## using the 'ModelTox' class DLE model 
## DLEmodel e.g 'LogisticIndepBeta' class
Effmodel<- EffFlexi(Eff=c(1.223, 2.513),Effdose=c(25,300),
                    sigma2=c(a=0.1,b=0.1),
                    sigma2betaW=c(a=20,b=50),smooth="RW2",data=data)

##DLE and efficacy samples must be of 'Samples' Class
DLEsamples<-mcmc(data,DLEmodel,options)
Effsamples<-mcmc(data,Effmodel,options)

##target probabilities of the occurrence of a DLE during trial and at the 
## end of trial are defined as 0.35 and 0.3, respectively
## Using 30% posterior quantile of the TD35 and TD30 samples as estimates of 
## TD35 and TD30, function specified in TDderive slot
## Using the 50% posterio quantile of the Gstar (the dose which gives the maximum 
## gain value) samples as Gstar estimate,function specified in Gstarderive slot 
mynextbest<-NextBestMaxGainSamples(DLEDuringTrialtarget=0.35,
                                   DLEEndOfTrialtarget=0.3,
                                   TDderive=function(TDsamples){
                                     quantile(TDsamples,prob=0.3)},
                                   Gstarderive=function(Gstarsamples){
                                     quantile(Gstarsamples,prob=0.5)})

RecommendDose<-nextBest(mynextbest,doselimit=max(data@doseGrid),samples=DLEsamples,
                        model=DLEmodel,
                        data=data,Effmodel=Effmodel,Effsamples=Effsamples)

The virtual class for finding next best dose

Description

The virtual class for finding next best dose

Initialization function for "NextBestDualEndpoint"

Description

Initialization function for "NextBestDualEndpoint"

Usage

NextBestDualEndpoint(
  target,
  scale = c("relative", "absolute"),
  overdose,
  maxOverdoseProb,
  targetThresh = 0.01
)
NextBestDualEndpoint(
  target,
  scale = c("relative", "absolute"),
  overdose,
  maxOverdoseProb,
  targetThresh = 0.01
)

Arguments

`target`	see `NextBestDualEndpoint`
`scale`	see `NextBestDualEndpoint`
`overdose`	see `NextBestDualEndpoint`
`maxOverdoseProb`	see `NextBestDualEndpoint`
`targetThresh`	see `NextBestDualEndpoint`

Value

the NextBestDualEndpoint object

The class with the input for finding the next dose based on the dual endpoint model

Description

This rule first excludes all doses that exceed the probability maxOverdoseProb of having an overdose toxicity, as specified by the overdose interval overdose. Then, it picks under the remaining admissible doses the one that maximizes the probability to be in the target biomarker range, by default relative to the maximum biomarker level across the dose grid or relative to the Emax parameter in case a parametric model was selected (e.g. DualEndpointBeta, DualEndpointEmax)) However, is scale is set to "absolute" then the natural absolute biomarker scale can be used to set a target.

Slots

target: the biomarker target range, that needs to be reached. For example, (0.8, 1.0) and scale="relative" means we target a dose with at least 80% of maximum biomarker level. As an other example, (0.5, 0.8) would mean that we target a dose between 50% and 80% of the maximum biomarker level.
scale: either relative (default, then the target is interpreted relative to the maximum, so must be a probability range) or absolute (then the target is interpreted as absolute biomarker range)
overdose: the overdose toxicity interval (lower limit excluded, upper limit included)
maxOverdoseProb: maximum overdose probability that is allowed
targetThresh: which target probability threshold needs to be fulfilled before the target probability will be used for deriving the next best dose (default: 0.01)

Examples


# Target a dose achieving at least 0.9 of maximum biomarker level (efficacy)
# and with a probability below 0.25 that prob(DLT)>0.35 (safety)

myNextBest <- NextBestDualEndpoint(target=c(0.9, 1),
                                   overdose=c(0.35, 1),
                                   maxOverdoseProb=0.25)

## now do it with an absolute target on the natural biomarker scale:

myNextBest <- NextBestDualEndpoint(target=c(200, 300),
                                   scale="absolute",
                                   overdose=c(0.35, 1),
                                   maxOverdoseProb=0.25)
# Target a dose achieving at least 0.9 of maximum biomarker level (efficacy)
# and with a probability below 0.25 that prob(DLT)>0.35 (safety)

myNextBest <- NextBestDualEndpoint(target=c(0.9, 1),
                                   overdose=c(0.35, 1),
                                   maxOverdoseProb=0.25)

## now do it with an absolute target on the natural biomarker scale:

myNextBest <- NextBestDualEndpoint(target=c(200, 300),
                                   scale="absolute",
                                   overdose=c(0.35, 1),
                                   maxOverdoseProb=0.25)

Initialization function for the class 'NextBestMaxGain'

Description

Initialization function for the class 'NextBestMaxGain'

Usage

NextBestMaxGain(DLEDuringTrialtarget, DLEEndOfTrialtarget)
NextBestMaxGain(DLEDuringTrialtarget, DLEEndOfTrialtarget)

Arguments

`DLEDuringTrialtarget`	please refer to `NextBestMaxGain` class object
`DLEEndOfTrialtarget`	please refer to `NextBestMaxGain` class object

Value

the NextBestMaxGain class object

Next best dose with maximum gain value based on a pseudo DLE and efficacy model without samples

Description

This is a class for which to find the next dose which is safe and give the maximum gain value for allocation. This is a class where no DLE and efficacy samples are involved. This is only based on the probabilities of the occurrence of a DLE and the values of the mean efficacy responses obtained by using the modal estimates of the DLE and efficacy model parameters. There are two inputs which are the two target probabilities of the occurrence of a DLE used during trial and used at the end of trial, for finding the next best dose that is safe and gives the maximum gain value and the dose to recommend at the end of a trial. This is only suitable to use with DLE models specified in 'ModelTox' class and efficacy models specified in 'ModelEff' (except 'EffFlexi' model) class

Slots

DLEDuringTrialtarget: the target probability of the occurrrence of a DLE to be used during the trial
DLEEndOfTrialtarget: the target probability of the occurrence of a DLE to be used at the end of the trial. This target is particularly used to recommend the dose for which its posterior probability of the occurrence of a DLE is equal to this target

Examples

##define the NextBestMaxGain class (no samples and based a pseudo DLE model and a efficacy model)
##specified the target probability of the occurrence of a DLE during the trial be 0.35
##specified the target probability of the occurrence of a DLE at the end of trial be 0.3

myNextBest <-NextBestMaxGain(DLEDuringTrialtarget=0.35,
                             DLEEndOfTrialtarget=0.3)
##define the NextBestMaxGain class (no samples and based a pseudo DLE model and a efficacy model)
##specified the target probability of the occurrence of a DLE during the trial be 0.35
##specified the target probability of the occurrence of a DLE at the end of trial be 0.3

myNextBest <-NextBestMaxGain(DLEDuringTrialtarget=0.35,
                             DLEEndOfTrialtarget=0.3)

Initialization function for class "NextBestMaxGainSamples"

Description

Initialization function for class "NextBestMaxGainSamples"

Usage

NextBestMaxGainSamples(
  DLEDuringTrialtarget,
  DLEEndOfTrialtarget,
  TDderive,
  Gstarderive
)
NextBestMaxGainSamples(
  DLEDuringTrialtarget,
  DLEEndOfTrialtarget,
  TDderive,
  Gstarderive
)

Arguments

`DLEDuringTrialtarget`	please refer to `NextBestMaxGainSamples` class object
`DLEEndOfTrialtarget`	please refer to `NextBestMaxGainSamples` class object
`TDderive`	please refer to `NextBestMaxGainSamples` class object
`Gstarderive`	please refer to `NextBestMaxGainSamples` class object

Value

the NextBestMaxGainSamples class object

Next best dose with maximum gain value based on a pseudo DLE and efficacy model with samples

Description

This is a class for which to find the next dose which is safe and give the maximum gain value for allocation. This is a class where DLE and efficacy samples are involved. There are two inputs which are the two target probabilities of the occurrence of a DLE used during trial and used at the end of trial, for finding the next best dose that is safe and gives the maximum gain value and the dose to recommend at the end of a trial. This is only suitable to use with DLE models specified in 'ModelTox' class and efficacy models specified in 'ModelEff' class class

Slots

DLEDuringTrialtarget: the target probability of the occurrrence of a DLE to be used during the trial
DLEEndOfTrialtarget: the target probability of the occurrence of a DLE to be used at the end of the trial. This target is particularly used to recommend the dose for which its posterior probability of the occurrence of a DLE is equal to this target
TDderive: the function which derives from the input, a vector of the posterior samples called TDsamples of the dose which has the probability of the occurrence of DLE equals to either the targetDuringTrial or targetEndOfTrial, the final next best TDtargetDuringTrial (the dose with probability of the occurrence of DLE equals to the targetDuringTrial)and TDtargetEndOfTrial estimate.
Gstarderive: the function which derives from the input, a vector of the posterior Gstar (the dose which gives the maximum gain value) samples called Gstarsamples, the final next best Gstar estimate.

Examples

##define the NextBestMaxGainsamples class
##specified the target probability of the occurrence of a DLE during the trial be 0.35
##specified the target probability of the occurrence of a DLE at the end of trial be 0.3
## we want the use the 30% posterior quantile (the 30th percentaile) of the TD35 
## (the dose level with probability of DLE equals 0.35) and TD30 samples
## For Gstar (the dose which gives tha maximum
##gain) samples, we will use the 50% posterio quantile (the median or th 50th percentile) 
## of the Gstar sample
##A function is then defined in the 'TDderive' slot for the TD30 and TD35 samples
## and another function is defined in the 'Gstarderive' slot for Gstar samples
mynextbest<-NextBestMaxGainSamples(DLEDuringTrialtarget=0.35,
                                   DLEEndOfTrialtarget=0.3,
                                   TDderive=function(TDsamples){
                                     quantile(TDsamples,prob=0.3)},
                                   Gstarderive=function(Gstarsamples){
                                     quantile(Gstarsamples,prob=0.5)})
##define the NextBestMaxGainsamples class
##specified the target probability of the occurrence of a DLE during the trial be 0.35
##specified the target probability of the occurrence of a DLE at the end of trial be 0.3
## we want the use the 30% posterior quantile (the 30th percentaile) of the TD35 
## (the dose level with probability of DLE equals 0.35) and TD30 samples
## For Gstar (the dose which gives tha maximum
##gain) samples, we will use the 50% posterio quantile (the median or th 50th percentile) 
## of the Gstar sample
##A function is then defined in the 'TDderive' slot for the TD30 and TD35 samples
## and another function is defined in the 'Gstarderive' slot for Gstar samples
mynextbest<-NextBestMaxGainSamples(DLEDuringTrialtarget=0.35,
                                   DLEEndOfTrialtarget=0.3,
                                   TDderive=function(TDsamples){
                                     quantile(TDsamples,prob=0.3)},
                                   Gstarderive=function(Gstarsamples){
                                     quantile(Gstarsamples,prob=0.5)})

Initialization function for class "NextBestMTD"

Description

Initialization function for class "NextBestMTD"

Usage

NextBestMTD(target, derive)
NextBestMTD(target, derive)

Arguments

`target`	see `NextBestMTD`
`derive`	see `NextBestMTD`

Value

the NextBestMTD object

The class with the input for finding the next best MTD estimate

Description

The class with the input for finding the next best MTD estimate

Slots

target: the target toxicity probability
derive: the function which derives from the input, a vector of posterior MTD samples called mtdSamples, the final next best MTD estimate.

Examples


# In the example below the MTD is defined as the dose for which prob(DLE)=0.33 and
# we will use the 25th quantile of the posterior of MTD as our next best dose.
mtdNextBest <- NextBestMTD(target=0.33,
                           derive=
                             function(mtdSamples){
                               quantile(mtdSamples, probs=0.25)
                             })


# In the example below the MTD is defined as the dose for which prob(DLE)=0.33 and
# we will use the 25th quantile of the posterior of MTD as our next best dose.
mtdNextBest <- NextBestMTD(target=0.33,
                           derive=
                             function(mtdSamples){
                               quantile(mtdSamples, probs=0.25)
                             })

Initialization function for "NextBestNCRM"

Description

Initialization function for "NextBestNCRM"

Usage

NextBestNCRM(target, overdose, maxOverdoseProb)
NextBestNCRM(target, overdose, maxOverdoseProb)

Arguments

`target`	see `NextBestNCRM`
`overdose`	see `NextBestNCRM`
`maxOverdoseProb`	see `NextBestNCRM`

Value

the NextBestNCRM object

The class with the input for finding the next dose in target interval

Description

Note that to avoid numerical problems, the dose selection algorithm has been implemented as follows: First admissible doses are found, which are those with probability to fall in overdose category being below maxOverdoseProb. Next, within the admissible doses, the maximum probability to fall in the target category is calculated. If that is above 5% (i.e., it is not just numerical error), then the corresponding dose is the next recommended dose. Otherwise, the highest admissible dose is the next recommended dose.

Slots

target: the target toxicity interval (limits included)
overdose: the overdose toxicity interval (lower limit excluded, upper limit included)
maxOverdoseProb: maximum overdose probability that is allowed

Examples


# In the example below, the target toxiciy interval [0.2, 0.35] while the 
# overdose interval is (0.35,1]. Finally we would like to constrain the probability
# of overdosing below 25%.
myNextBest <- NextBestNCRM(target=c(0.2, 0.35),
                           overdose=c(0.35, 1),
                           maxOverdoseProb=0.25)


# In the example below, the target toxiciy interval [0.2, 0.35] while the 
# overdose interval is (0.35,1]. Finally we would like to constrain the probability
# of overdosing below 25%.
myNextBest <- NextBestNCRM(target=c(0.2, 0.35),
                           overdose=c(0.35, 1),
                           maxOverdoseProb=0.25)

Initialization function for the class "NextBestTD"

Description

Initialization function for the class "NextBestTD"

Usage

NextBestTD(targetDuringTrial, targetEndOfTrial)
NextBestTD(targetDuringTrial, targetEndOfTrial)

Arguments

`targetDuringTrial`	please refer to `NextBestTD` class object
`targetEndOfTrial`	please refer to `NextBestTD` class object

Value

the NextBestTD class object

Next best dose based on Pseudo DLE model without sample

Description

The class is to find the next best dose for allocation and the dose for final recommendation at the end of a trial without involving any samples. This is a class for which only DLE response will be incorporated for the dose-allocation. This is only based on the probabilities of the occurrence of a DLE obtained by using the modal estimates of the model paramters. There are two inputs inputs which are the two target probabilities of the occurrence of a DLE used during trial and used at the end of trial, for finding the next best dose for allocation and the dose for recommendation at the end of the trial. It is only suitable to use with the model specified in ModelTox class.

Slots

targetDuringTrial: the target probability of the occurrrence of a DLE to be used during the trial
targetEndOfTrial: the target probability of the occurrence of a DLE to be used at the end of the trial. This target is particularly used to recommend the dose for which its posterior probability of the occurrence of a DLE is equal to this target

Examples

##define the NextBestTD class (no samples and based a pseudo DLE model)
##specified the target probability of the occurrence of a DLE during the trial be 0.35
##specified the target probability of the occurrence of a DLE at the end of trial be 0.3

myNextBest <-NextBestTD(targetDuringTrial=0.35,
                        targetEndOfTrial=0.3)
##define the NextBestTD class (no samples and based a pseudo DLE model)
##specified the target probability of the occurrence of a DLE during the trial be 0.35
##specified the target probability of the occurrence of a DLE at the end of trial be 0.3

myNextBest <-NextBestTD(targetDuringTrial=0.35,
                        targetEndOfTrial=0.3)

Initialization function for class "NextBestTDsamples"

Description

Initialization function for class "NextBestTDsamples"

Usage

NextBestTDsamples(targetDuringTrial, targetEndOfTrial, derive)
NextBestTDsamples(targetDuringTrial, targetEndOfTrial, derive)

Arguments

`targetDuringTrial`	please refer to `NextBestTDsamples` class object
`targetEndOfTrial`	please refer to `NextBestTDsamples` class object
`derive`	please refer to `NextBestTDsamples` class object

Value

the NextBestTDsamples class object

Next best dose based on Pseudo DLE Model with samples

Description

The class is to find the next best dose for allocation and the dose for final recommendation at the end of a trial. There are two input target probabilities of the occurrence of a DLE used during trial and used at the end of trial to find the two doses. For this class, only DLE response will be incorporated for the dose allocation and DLEsamples must be used to obtain the next dose for allocation.

Slots

targetDuringTrial: the target probability of the occurrrence of a DLE to be used during the trial
targetEndOfTrial: the target probability of the occurrence of a DLE to be used at the end of the trial. This target is particularly used to recommend the dose at the end of a trial for which its posterior probability of the occurrence of a DLE is equal to this target
derive: the function which derives from the input, a vector of the posterior samples called TDsamples of the dose which has the probability of the occurrence of DLE equals to either the targetDuringTrial or targetEndOfTrial, the final next best TDtargetDuringTrial (the dose with probability of the occurrence of DLE equals to the targetDuringTrial)and TDtargetEndOfTrial estimate.

Examples

##define the NextBestTDsamples class
##specified the target probability of the occurrence of a DLE during the trial be 0.35
##specified the target probability of the occurrence of a DLE at the end of trial be 0.3
## we want the use the 30% posterior quantile (the 30th percentaile) of the TD35 (the dose 
## level with probability of DLE equals 0.35) and TD30 samples. A function is then defined 
## in the 'derive' slot
myNextBest <-NextBestTDsamples(targetDuringTrial=0.35,
                               targetEndOfTrial=0.3,
                               derive=function(TDsamples){quantile(TDsamples,probs=0.3)})
##define the NextBestTDsamples class
##specified the target probability of the occurrence of a DLE during the trial be 0.35
##specified the target probability of the occurrence of a DLE at the end of trial be 0.3
## we want the use the 30% posterior quantile (the 30th percentaile) of the TD35 (the dose 
## level with probability of DLE equals 0.35) and TD30 samples. A function is then defined 
## in the 'derive' slot
myNextBest <-NextBestTDsamples(targetDuringTrial=0.35,
                               targetEndOfTrial=0.3,
                               derive=function(TDsamples){quantile(TDsamples,probs=0.3)})

Initialization function for "NextBestThreePlusThree"

Description

Initialization function for "NextBestThreePlusThree"

Usage

NextBestThreePlusThree()
NextBestThreePlusThree()

Value

the NextBestThreePlusThree object

The class with the input for finding the next dose in target interval

Description

Implements the classical 3+3 dose recommendation. No input is required, hence this class has no slots.

Examples


## Choose the next best dose using the  classical 3+3 design.
## No input is required, hence this function has no parameters.
myNextBest <- NextBestThreePlusThree()


## Choose the next best dose using the  classical 3+3 design.
## No input is required, hence this function has no parameters.
myNextBest <- NextBestThreePlusThree()

The method combining two atomic stopping rules

Description

The method combining two atomic stopping rules

Usage

## S4 method for signature 'Stopping,Stopping'
e1 | e2
## S4 method for signature 'Stopping,Stopping'
e1 | e2

Arguments

`e1`	First `Stopping` object
`e2`	Second `Stopping` object

Value

The StoppingAny object

Examples


## Example of combining two atomic stopping rules with an OR ('|') operator

myStopping1 <- StoppingMinCohorts(nCohorts=3)
myStopping2 <- StoppingTargetProb(target=c(0.2, 0.35),
                                  prob=0.5)

myStopping <- myStopping1 | myStopping2 




## Example of combining two atomic stopping rules with an OR ('|') operator

myStopping1 <- StoppingMinCohorts(nCohorts=3)
myStopping2 <- StoppingTargetProb(target=c(0.2, 0.35),
                                  prob=0.5)

myStopping <- myStopping1 | myStopping2

The method combining a stopping list and an atomic

Description

The method combining a stopping list and an atomic

Usage

## S4 method for signature 'StoppingAny,Stopping'
e1 | e2
## S4 method for signature 'StoppingAny,Stopping'
e1 | e2

Arguments

`e1`	`StoppingAny` object
`e2`	`Stopping` object

Value

The modified StoppingAny object

Examples


## Example of combining a list of stopping rules with an atomic stopping rule
## with an OR ('|') operator

myStopping1 <- StoppingMinCohorts(nCohorts=3)
myStopping2 <- StoppingTargetProb(target=c(0.2, 0.35),
                                  prob=0.5)

myStopping3 <- StoppingMinPatients(nPatients=20)

myStopping <- (myStopping1 & myStopping2 ) | myStopping3 




## Example of combining a list of stopping rules with an atomic stopping rule
## with an OR ('|') operator

myStopping1 <- StoppingMinCohorts(nCohorts=3)
myStopping2 <- StoppingTargetProb(target=c(0.2, 0.35),
                                  prob=0.5)

myStopping3 <- StoppingMinPatients(nPatients=20)

myStopping <- (myStopping1 & myStopping2 ) | myStopping3

The method combining an atomic and a stopping list

Description

The method combining an atomic and a stopping list

Usage

## S4 method for signature 'Stopping,StoppingAny'
e1 | e2
## S4 method for signature 'Stopping,StoppingAny'
e1 | e2

Arguments

`e1`	`Stopping` object
`e2`	`StoppingAny` object

Value

The modified StoppingAny object

Examples


## Example of combining an atomic stopping rule with a list of stopping rules
## with an OR ('|') operator

myStopping1 <- StoppingMinCohorts(nCohorts=3)
myStopping2 <- StoppingTargetProb(target=c(0.2, 0.35),
                                  prob=0.5)

myStopping3 <- StoppingMinPatients(nPatients=20)

myStopping <-  myStopping3 | (myStopping1 & myStopping2 ) 




## Example of combining an atomic stopping rule with a list of stopping rules
## with an OR ('|') operator

myStopping1 <- StoppingMinCohorts(nCohorts=3)
myStopping2 <- StoppingTargetProb(target=c(0.2, 0.35),
                                  prob=0.5)

myStopping3 <- StoppingMinPatients(nPatients=20)

myStopping <-  myStopping3 | (myStopping1 & myStopping2 )

Plot method for the "Data" class

Description

Plot method for the "Data" class

Usage

## S4 method for signature 'Data,missing'
plot(x, y, blind = FALSE, ...)
## S4 method for signature 'Data,missing'
plot(x, y, blind = FALSE, ...)

Arguments

`x`	the `Data` object we want to plot
`y`	missing
`blind`	Logical (default FALSE) if to blind the data. If TRUE, then placebo subjects are reported by the active dose level of the corresponding cohort and DLEs are always assigned to the firsts subjects.
`...`	not used

Value

the ggplot object

Examples


# Create some data of class 'Data'
myData <- Data(x=c(0.1,0.5,1.5,3,6,10,10,10),
               y=c(0,0,0,0,0,0,1,0),
               doseGrid=c(0.1,0.5,1.5,3,6,
                          seq(from=10,to=80,by=2)))

# Plot the data
plot(myData)

# Create some data of class 'Data'
myData <- Data(x=c(0.1,0.5,1.5,3,6,10,10,10),
               y=c(0,0,0,0,0,0,1,0),
               doseGrid=c(0.1,0.5,1.5,3,6,
                          seq(from=10,to=80,by=2)))

# Plot the data
plot(myData)

Plot of the fitted dose-tox based with a given pseudo DLE model and data without samples

Description

Plot of the fitted dose-tox based with a given pseudo DLE model and data without samples

Usage

## S4 method for signature 'Data,ModelTox'
plot(
  x,
  y,
  xlab = "Dose level",
  ylab = "Probability of DLE",
  showLegend = TRUE,
  ...
)
## S4 method for signature 'Data,ModelTox'
plot(
  x,
  y,
  xlab = "Dose level",
  ylab = "Probability of DLE",
  showLegend = TRUE,
  ...
)

Arguments

`x`	the data of `Data` class object
`y`	the model of the `ModelTox` class object
`xlab`	the x axis label
`ylab`	the y axis label
`showLegend`	should the legend be shown? (default)
`...`	not used

Value

This returns the ggplot object for the dose-DLE model plot

Examples

##plot the dose-DLE curve given a pseudo DLE model using data without samples
##data must be of 'Data' class 
##define the data 
data<-Data(x=c(25,50,50,75,100,100,225,300),y=c(0,0,0,0,1,1,1,1),
           doseGrid=seq(25,300,25))
##model must be from 'ModelTox' class e.g 'LogisticIndepBeta' class model
##define the model (see LogisticIndepBeta example)
model <-LogisticIndepBeta(binDLE=c(1.05,1.8),DLEweights=c(3,3),DLEdose=c(25,300),data=data)
## plot the dose-DLE curve
## 'x' is the data and 'y' is the model in plot
plot(x=data,y=model)
##plot the dose-DLE curve given a pseudo DLE model using data without samples
##data must be of 'Data' class 
##define the data 
data<-Data(x=c(25,50,50,75,100,100,225,300),y=c(0,0,0,0,1,1,1,1),
           doseGrid=seq(25,300,25))
##model must be from 'ModelTox' class e.g 'LogisticIndepBeta' class model
##define the model (see LogisticIndepBeta example)
model <-LogisticIndepBeta(binDLE=c(1.05,1.8),DLEweights=c(3,3),DLEdose=c(25,300),data=data)
## plot the dose-DLE curve
## 'x' is the data and 'y' is the model in plot
plot(x=data,y=model)

Plot method for the "DataDual" class

Description

Plot method for the "DataDual" class

Usage

## S4 method for signature 'DataDual,missing'
plot(x, y, blind = FALSE, ...)
## S4 method for signature 'DataDual,missing'
plot(x, y, blind = FALSE, ...)

Arguments

`x`	the `DataDual` object we want to plot
`y`	missing
`blind`	Logical (default FALSE) if to blind the data
`...`	not used

Value

the ggplot object

Examples


# Create some data of class 'DataDual'
myData <- DataDual(x=c(0.1,0.5,1.5,3,6,10,10,10),
                   y=c(0,0,0,0,0,0,1,0),
                   w=rnorm(8),
                   doseGrid=c(0.1,0.5,1.5,3,6,
                              seq(from=10,to=80,by=2)))

# Plot the data
#grid.arrange(plot(myData))

plot(myData)
# Create some data of class 'DataDual'
myData <- DataDual(x=c(0.1,0.5,1.5,3,6,10,10,10),
                   y=c(0,0,0,0,0,0,1,0),
                   w=rnorm(8),
                   doseGrid=c(0.1,0.5,1.5,3,6,
                              seq(from=10,to=80,by=2)))

# Plot the data
#grid.arrange(plot(myData))

plot(myData)

Plot of the fitted dose-efficacy based with a given pseudo efficacy model and data without samples

Description

Plot of the fitted dose-efficacy based with a given pseudo efficacy model and data without samples

Usage

## S4 method for signature 'DataDual,ModelEff'
plot(
  x,
  y,
  ...,
  xlab = "Dose level",
  ylab = "Expected Efficacy",
  showLegend = TRUE
)
## S4 method for signature 'DataDual,ModelEff'
plot(
  x,
  y,
  ...,
  xlab = "Dose level",
  ylab = "Expected Efficacy",
  showLegend = TRUE
)

Arguments

`x`	the data of `DataDual` class object
`y`	the model of the `ModelEff` class object
`...`	not used
`xlab`	the x axis label
`ylab`	the y axis label
`showLegend`	should the legend be shown? (default)

Value

This returns the ggplot object for the dose-efficacy model plot

Examples

##plot the dose-efficacy curve given a pseudo efficacy model using data without samples
##data must be of 'DataDual' class 
##define the data 
data<-DataDual(x=c(25,50,50,75,100,100,225,300),y=c(0,0,0,0,1,1,1,1),
               w=c(0.31,0.42,0.59,0.45,0.6,0.7,0.6,0.52),
               doseGrid=seq(25,300,25),placebo=FALSE)
##model must be from 'ModelEff' class e.g 'Effloglog' class model
##define the model (see Effloglog example)
Effmodel<-Effloglog(Eff=c(1.223,2.513),Effdose=c(25,300),nu=c(a=1,b=0.025),data=data,c=0)
## plot the dose-efficacy curve
## 'x' is the data and 'y' is the model in plot
plot(x=data,y=Effmodel)
##plot the dose-efficacy curve given a pseudo efficacy model using data without samples
##data must be of 'DataDual' class 
##define the data 
data<-DataDual(x=c(25,50,50,75,100,100,225,300),y=c(0,0,0,0,1,1,1,1),
               w=c(0.31,0.42,0.59,0.45,0.6,0.7,0.6,0.52),
               doseGrid=seq(25,300,25),placebo=FALSE)
##model must be from 'ModelEff' class e.g 'Effloglog' class model
##define the model (see Effloglog example)
Effmodel<-Effloglog(Eff=c(1.223,2.513),Effdose=c(25,300),nu=c(a=1,b=0.025),data=data,c=0)
## plot the dose-efficacy curve
## 'x' is the data and 'y' is the model in plot
plot(x=data,y=Effmodel)

Plot dual-endpoint simulations

Description

This plot method can be applied to DualSimulations objects in order to summarize them graphically. In addition to the standard plot types, there is

sigma2W: Plot a boxplot of the final biomarker variance estimates in the simulated trials
rho: Plot a boxplot of the final correlation estimates in the simulated trials

Usage

## S4 method for signature 'DualSimulations,missing'
plot(x, y, type = c("trajectory", "dosesTried", "sigma2W", "rho"), ...)
## S4 method for signature 'DualSimulations,missing'
plot(x, y, type = c("trajectory", "dosesTried", "sigma2W", "rho"), ...)

Arguments

`x`	the `DualSimulations` object we want to plot from
`y`	missing
`type`	the type of plots you want to obtain.
`...`	not used

Value

A single ggplot object if a single plot is asked for, otherwise a gtable object.

Examples


# Define the dose-grid
emptydata <- DataDual(doseGrid = c(1, 3, 5, 10, 15, 20, 25, 40, 50, 80, 100))

# Initialize the CRM model 
model <- DualEndpointRW(mu = c(0, 1),
                        Sigma = matrix(c(1, 0, 0, 1), nrow=2),
                        sigma2betaW = 0.01,
                        sigma2W = c(a=0.1, b=0.1),
                        rho = c(a=1, b=1),
                        smooth="RW1")

# Choose the rule for selecting the next dose 
myNextBest <- NextBestDualEndpoint(target=c(0.9, 1),
                                   overdose=c(0.35, 1),
                                   maxOverdoseProb=0.25)

# Choose the rule for the cohort-size 
mySize1 <- CohortSizeRange(intervals=c(0, 30),
                           cohortSize=c(1, 3))
mySize2 <- CohortSizeDLT(DLTintervals=c(0, 1),
                         cohortSize=c(1, 3))
mySize <- maxSize(mySize1, mySize2)

# Choose the rule for stopping
myStopping4 <- StoppingTargetBiomarker(target=c(0.9, 1),
                                       prob=0.5)
myStopping <- myStopping4 | StoppingMinPatients(40)

# Choose the rule for dose increments
myIncrements <- IncrementsRelative(intervals=c(0, 20),
                                   increments=c(1, 0.33))

# Initialize the design
design <- DualDesign(model = model,
                     data = emptydata,
                     nextBest = myNextBest,
                     stopping = myStopping,
                     increments = myIncrements,
                     cohortSize = CohortSizeConst(3),
                     startingDose = 3)
  
# define scenarios for the TRUE toxicity and efficacy profiles
betaMod <- function (dose, e0, eMax, delta1, delta2, scal)
{
  maxDens <- (delta1^delta1) * (delta2^delta2)/((delta1 + delta2)^(delta1 + delta2))
  dose <- dose/scal
  e0 + eMax/maxDens * (dose^delta1) * (1 - dose)^delta2
}

trueBiomarker <- function(dose)
{
  betaMod(dose, e0=0.2, eMax=0.6, delta1=5, delta2=5 * 0.5 / 0.5, scal=100)
}

trueTox <- function(dose)
{
  pnorm((dose-60)/10)
}

# Draw the TRUE profiles
par(mfrow=c(1, 2))
curve(trueTox(x), from=0, to=80)
curve(trueBiomarker(x), from=0, to=80)

# Run the simulation on the desired design
# We only generate 1 trial outcome here for illustration, for the actual study 
##Also for illustration purpose, we will use 5 burn-ins to generate 20 samples
# this should be increased of course
mySims <- simulate(design,
                   trueTox=trueTox,
                   trueBiomarker=trueBiomarker,
                   sigma2W=0.01,
                   rho=0,
                   nsim=1,
                   parallel=FALSE,
                   seed=3,
                   startingDose=6,
                   mcmcOptions =
                     McmcOptions(burnin=5,
                                 step=1,
                                 samples=20))

# Plot the results of the simulation
print(plot(mySims))



  
# Define the dose-grid
emptydata <- DataDual(doseGrid = c(1, 3, 5, 10, 15, 20, 25, 40, 50, 80, 100))

# Initialize the CRM model 
model <- DualEndpointRW(mu = c(0, 1),
                        Sigma = matrix(c(1, 0, 0, 1), nrow=2),
                        sigma2betaW = 0.01,
                        sigma2W = c(a=0.1, b=0.1),
                        rho = c(a=1, b=1),
                        smooth="RW1")

# Choose the rule for selecting the next dose 
myNextBest <- NextBestDualEndpoint(target=c(0.9, 1),
                                   overdose=c(0.35, 1),
                                   maxOverdoseProb=0.25)

# Choose the rule for the cohort-size 
mySize1 <- CohortSizeRange(intervals=c(0, 30),
                           cohortSize=c(1, 3))
mySize2 <- CohortSizeDLT(DLTintervals=c(0, 1),
                         cohortSize=c(1, 3))
mySize <- maxSize(mySize1, mySize2)

# Choose the rule for stopping
myStopping4 <- StoppingTargetBiomarker(target=c(0.9, 1),
                                       prob=0.5)
myStopping <- myStopping4 | StoppingMinPatients(40)

# Choose the rule for dose increments
myIncrements <- IncrementsRelative(intervals=c(0, 20),
                                   increments=c(1, 0.33))

# Initialize the design
design <- DualDesign(model = model,
                     data = emptydata,
                     nextBest = myNextBest,
                     stopping = myStopping,
                     increments = myIncrements,
                     cohortSize = CohortSizeConst(3),
                     startingDose = 3)
  
# define scenarios for the TRUE toxicity and efficacy profiles
betaMod <- function (dose, e0, eMax, delta1, delta2, scal)
{
  maxDens <- (delta1^delta1) * (delta2^delta2)/((delta1 + delta2)^(delta1 + delta2))
  dose <- dose/scal
  e0 + eMax/maxDens * (dose^delta1) * (1 - dose)^delta2
}

trueBiomarker <- function(dose)
{
  betaMod(dose, e0=0.2, eMax=0.6, delta1=5, delta2=5 * 0.5 / 0.5, scal=100)
}

trueTox <- function(dose)
{
  pnorm((dose-60)/10)
}

# Draw the TRUE profiles
par(mfrow=c(1, 2))
curve(trueTox(x), from=0, to=80)
curve(trueBiomarker(x), from=0, to=80)

# Run the simulation on the desired design
# We only generate 1 trial outcome here for illustration, for the actual study 
##Also for illustration purpose, we will use 5 burn-ins to generate 20 samples
# this should be increased of course
mySims <- simulate(design,
                   trueTox=trueTox,
                   trueBiomarker=trueBiomarker,
                   sigma2W=0.01,
                   rho=0,
                   nsim=1,
                   parallel=FALSE,
                   seed=3,
                   startingDose=6,
                   mcmcOptions =
                     McmcOptions(burnin=5,
                                 step=1,
                                 samples=20))

# Plot the results of the simulation
print(plot(mySims))

Plot summaries of the dual-endpoint design simulations

Description

This plot method can be applied to DualSimulationsSummary objects in order to summarize them graphically. Possible type of plots at the moment are those listed in plot,SimulationsSummary,missing-method plus:

meanBiomarkerFit: Plot showing the average fitted dose-biomarker curve across the trials, together with 95% credible intervals, and comparison with the assumed truth (as specified by the trueBiomarker argument to summary,DualSimulations-method)

You can specify any subset of these in the type argument.

Usage

## S4 method for signature 'DualSimulationsSummary,missing'
plot(
  x,
  y,
  type = c("nObs", "doseSelected", "propDLTs", "nAboveTarget", "meanFit",
    "meanBiomarkerFit"),
  ...
)
## S4 method for signature 'DualSimulationsSummary,missing'
plot(
  x,
  y,
  type = c("nObs", "doseSelected", "propDLTs", "nAboveTarget", "meanFit",
    "meanBiomarkerFit"),
  ...
)

Arguments

`x`	the `DualSimulationsSummary` object we want to plot from
`y`	missing
`type`	the types of plots you want to obtain.
`...`	not used

Value

A single ggplot object if a single plot is asked for, otherwise a gtable object.

Examples


# Define the dose-grid
emptydata <- DataDual(doseGrid = c(1, 3, 5, 10, 15, 20, 25, 40, 50, 80, 100))

# Initialize the CRM model 
model <- DualEndpointRW(mu = c(0, 1),
                        Sigma = matrix(c(1, 0, 0, 1), nrow=2),
                        sigma2betaW = 0.01,
                        sigma2W = c(a=0.1, b=0.1),
                        rho = c(a=1, b=1),
                        smooth="RW1")

# Choose the rule for selecting the next dose 
myNextBest <- NextBestDualEndpoint(target=c(0.9, 1),
                                   overdose=c(0.35, 1),
                                   maxOverdoseProb=0.25)

# Choose the rule for the cohort-size 
mySize1 <- CohortSizeRange(intervals=c(0, 30),
                           cohortSize=c(1, 3))
mySize2 <- CohortSizeDLT(DLTintervals=c(0, 1),
                         cohortSize=c(1, 3))
mySize <- maxSize(mySize1, mySize2)

# Choose the rule for stopping
myStopping4 <- StoppingTargetBiomarker(target=c(0.9, 1),
                                       prob=0.5)
# only 10 patients here for illustration!
myStopping <- myStopping4 | StoppingMinPatients(10)

# Choose the rule for dose increments
myIncrements <- IncrementsRelative(intervals=c(0, 20),
                                   increments=c(1, 0.33))

# Initialize the design
design <- DualDesign(model = model,
                     data = emptydata,
                     nextBest = myNextBest,
                     stopping = myStopping,
                     increments = myIncrements,
                     cohortSize = CohortSizeConst(3),
                     startingDose = 3)
  
# define scenarios for the TRUE toxicity and efficacy profiles
betaMod <- function (dose, e0, eMax, delta1, delta2, scal)
{
  maxDens <- (delta1^delta1) * (delta2^delta2)/((delta1 + delta2)^(delta1 + delta2))
  dose <- dose/scal
  e0 + eMax/maxDens * (dose^delta1) * (1 - dose)^delta2
}

trueBiomarker <- function(dose)
{
  betaMod(dose, e0=0.2, eMax=0.6, delta1=5, delta2=5 * 0.5 / 0.5, scal=100)
}

trueTox <- function(dose)
{
  pnorm((dose-60)/10)
}

# Draw the TRUE profiles
par(mfrow=c(1, 2))
curve(trueTox(x), from=0, to=80)
curve(trueBiomarker(x), from=0, to=80)

# Run the simulation on the desired design
# We only generate 1 trial outcome here for illustration, for the actual study
##For illustration purpose we will use 5 burn-ins to generate 20 samples
# this should be increased of course
mySims <- simulate(design,
                   trueTox=trueTox,
                   trueBiomarker=trueBiomarker,
                   sigma2W=0.01,
                   rho=0,
                   nsim=1,
                   parallel=FALSE,
                   seed=3,
                   startingDose=6,
                   mcmcOptions =
                     McmcOptions(burnin=5,
                                 step=1,
                                 samples=20))

# Plot the summary of the Simulations
plot(summary(mySims,
             trueTox = trueTox,
             trueBiomarker = trueBiomarker))



  
# Define the dose-grid
emptydata <- DataDual(doseGrid = c(1, 3, 5, 10, 15, 20, 25, 40, 50, 80, 100))

# Initialize the CRM model 
model <- DualEndpointRW(mu = c(0, 1),
                        Sigma = matrix(c(1, 0, 0, 1), nrow=2),
                        sigma2betaW = 0.01,
                        sigma2W = c(a=0.1, b=0.1),
                        rho = c(a=1, b=1),
                        smooth="RW1")

# Choose the rule for selecting the next dose 
myNextBest <- NextBestDualEndpoint(target=c(0.9, 1),
                                   overdose=c(0.35, 1),
                                   maxOverdoseProb=0.25)

# Choose the rule for the cohort-size 
mySize1 <- CohortSizeRange(intervals=c(0, 30),
                           cohortSize=c(1, 3))
mySize2 <- CohortSizeDLT(DLTintervals=c(0, 1),
                         cohortSize=c(1, 3))
mySize <- maxSize(mySize1, mySize2)

# Choose the rule for stopping
myStopping4 <- StoppingTargetBiomarker(target=c(0.9, 1),
                                       prob=0.5)
# only 10 patients here for illustration!
myStopping <- myStopping4 | StoppingMinPatients(10)

# Choose the rule for dose increments
myIncrements <- IncrementsRelative(intervals=c(0, 20),
                                   increments=c(1, 0.33))

# Initialize the design
design <- DualDesign(model = model,
                     data = emptydata,
                     nextBest = myNextBest,
                     stopping = myStopping,
                     increments = myIncrements,
                     cohortSize = CohortSizeConst(3),
                     startingDose = 3)
  
# define scenarios for the TRUE toxicity and efficacy profiles
betaMod <- function (dose, e0, eMax, delta1, delta2, scal)
{
  maxDens <- (delta1^delta1) * (delta2^delta2)/((delta1 + delta2)^(delta1 + delta2))
  dose <- dose/scal
  e0 + eMax/maxDens * (dose^delta1) * (1 - dose)^delta2
}

trueBiomarker <- function(dose)
{
  betaMod(dose, e0=0.2, eMax=0.6, delta1=5, delta2=5 * 0.5 / 0.5, scal=100)
}

trueTox <- function(dose)
{
  pnorm((dose-60)/10)
}

# Draw the TRUE profiles
par(mfrow=c(1, 2))
curve(trueTox(x), from=0, to=80)
curve(trueBiomarker(x), from=0, to=80)

# Run the simulation on the desired design
# We only generate 1 trial outcome here for illustration, for the actual study
##For illustration purpose we will use 5 burn-ins to generate 20 samples
# this should be increased of course
mySims <- simulate(design,
                   trueTox=trueTox,
                   trueBiomarker=trueBiomarker,
                   sigma2W=0.01,
                   rho=0,
                   nsim=1,
                   parallel=FALSE,
                   seed=3,
                   startingDose=6,
                   mcmcOptions =
                     McmcOptions(burnin=5,
                                 step=1,
                                 samples=20))

# Plot the summary of the Simulations
plot(summary(mySims,
             trueTox = trueTox,
             trueBiomarker = trueBiomarker))

Plot simulations

Description

Summarize the simulations with plots

Usage

## S4 method for signature 'GeneralSimulations,missing'
plot(x, y, type = c("trajectory", "dosesTried"), ...)
## S4 method for signature 'GeneralSimulations,missing'
plot(x, y, type = c("trajectory", "dosesTried"), ...)

Arguments

`x`	the `GeneralSimulations` object we want to plot from
`y`	missing
`type`	the type of plots you want to obtain.
`...`	not used

Details

This plot method can be applied to GeneralSimulations objects in order to summarize them graphically. Possible types of plots at the moment are:

trajectory: Summary of the trajectory of the simulated trials
dosesTried: Average proportions of the doses tested in patients

You can specify one or both of these in the type argument.

Value

A single ggplot object if a single plot is asked for, otherwise a gtable object.

Examples


##obtain the plot for the simulation results
##If only DLE responses are considered in the simulations

##Specified your simulations when no DLE samples are used
##Define your data set first using an empty data set 
## with dose levels from 25 to 300 with increments 25
data <- Data(doseGrid=seq(25,300,25))

##Specified the model of 'ModelTox' class eg 'LogisticIndepBeta' class model
model<-LogisticIndepBeta(binDLE=c(1.05,1.8),DLEweights=c(3,3),DLEdose=c(25,300),data=data)
##Then the escalation rule
tdNextBest <- NextBestTD(targetDuringTrial=0.35,
                         targetEndOfTrial=0.3)

## The cohort size, size of 3 subjects
mySize <-CohortSizeConst(size=3)
##Deifne the increments for the dose-escalation process
##The maximum increase of 200% for doses up to the maximum of the dose specified in the doseGrid
##The maximum increase of 200% for dose above the maximum of the dose specified in the doseGrid
##This is to specified a maximum of 3-fold restriction in dose-esclation
myIncrements<-IncrementsRelative(intervals=c(min(data@doseGrid),max(data@doseGrid)), 
                                 increments=c(2,2))
##Specified the stopping rule e.g stop when the maximum sample size of 12 patients has been reached
myStopping <- StoppingMinPatients(nPatients=12)
##Now specified the design with all the above information and starting with a dose of 25
design <- TDDesign(model=model,
                   nextBest=tdNextBest,
                   stopping=myStopping,
                   increments=myIncrements,
                   cohortSize=mySize,
                   data=data,startingDose=25)

##Specify the truth of the DLE responses
myTruth <- function(dose)
{ model@prob(dose, phi1=-53.66584, phi2=10.50499)
}

## Then specified the simulations and generate the trial 
##For illustration purpose only 1 simulation is produced (nsim=1). 
##The simulations
mySim <- simulate(design,
                  args=NULL,
                  truth=myTruth,
                  nsim=1,
                  seed=819,
                  parallel=FALSE)


##plot the simulations
print(plot(mySim))



##If DLE samples are involved
##The escalation rule
tdNextBest<-NextBestTDsamples(targetDuringTrial=0.35,
                              targetEndOfTrial=0.3,
                              derive=function(TDsamples){quantile(TDsamples,probs=0.3)})
##specify the design
design <- TDsamplesDesign(model=model,
                          nextBest=tdNextBest,
                          stopping=myStopping,
                          increments=myIncrements,
                          cohortSize=mySize,
                          data=data,startingDose=25)
##options for MCMC
##The simulations
##For illustration purpose only 1 simulation is produced (nsim=1). 
# mySim <- simulate(design,
#                   args=NULL,
#                   truth=myTruth,
#                   nsim=1,
#                   seed=819,
#                   mcmcOptions=options,
#                   parallel=FALSE)
# 
# ##plot the simulations
# print(plot(mySim))
# 
##obtain the plot for the simulation results
##If only DLE responses are considered in the simulations

##Specified your simulations when no DLE samples are used
##Define your data set first using an empty data set 
## with dose levels from 25 to 300 with increments 25
data <- Data(doseGrid=seq(25,300,25))

##Specified the model of 'ModelTox' class eg 'LogisticIndepBeta' class model
model<-LogisticIndepBeta(binDLE=c(1.05,1.8),DLEweights=c(3,3),DLEdose=c(25,300),data=data)
##Then the escalation rule
tdNextBest <- NextBestTD(targetDuringTrial=0.35,
                         targetEndOfTrial=0.3)

## The cohort size, size of 3 subjects
mySize <-CohortSizeConst(size=3)
##Deifne the increments for the dose-escalation process
##The maximum increase of 200% for doses up to the maximum of the dose specified in the doseGrid
##The maximum increase of 200% for dose above the maximum of the dose specified in the doseGrid
##This is to specified a maximum of 3-fold restriction in dose-esclation
myIncrements<-IncrementsRelative(intervals=c(min(data@doseGrid),max(data@doseGrid)), 
                                 increments=c(2,2))
##Specified the stopping rule e.g stop when the maximum sample size of 12 patients has been reached
myStopping <- StoppingMinPatients(nPatients=12)
##Now specified the design with all the above information and starting with a dose of 25
design <- TDDesign(model=model,
                   nextBest=tdNextBest,
                   stopping=myStopping,
                   increments=myIncrements,
                   cohortSize=mySize,
                   data=data,startingDose=25)

##Specify the truth of the DLE responses
myTruth <- function(dose)
{ model@prob(dose, phi1=-53.66584, phi2=10.50499)
}

## Then specified the simulations and generate the trial 
##For illustration purpose only 1 simulation is produced (nsim=1). 
##The simulations
mySim <- simulate(design,
                  args=NULL,
                  truth=myTruth,
                  nsim=1,
                  seed=819,
                  parallel=FALSE)


##plot the simulations
print(plot(mySim))



##If DLE samples are involved
##The escalation rule
tdNextBest<-NextBestTDsamples(targetDuringTrial=0.35,
                              targetEndOfTrial=0.3,
                              derive=function(TDsamples){quantile(TDsamples,probs=0.3)})
##specify the design
design <- TDsamplesDesign(model=model,
                          nextBest=tdNextBest,
                          stopping=myStopping,
                          increments=myIncrements,
                          cohortSize=mySize,
                          data=data,startingDose=25)
##options for MCMC
##The simulations
##For illustration purpose only 1 simulation is produced (nsim=1). 
# mySim <- simulate(design,
#                   args=NULL,
#                   truth=myTruth,
#                   nsim=1,
#                   seed=819,
#                   mcmcOptions=options,
#                   parallel=FALSE)
# 
# ##plot the simulations
# print(plot(mySim))
#

Graphical display of the general simulation summary

Description

This plot method can be applied to GeneralSimulationsSummary objects in order to summarize them graphically. Possible types of plots at the moment are:

Usage

## S4 method for signature 'GeneralSimulationsSummary,missing'
plot(x, y, type = c("nObs", "doseSelected", "propDLTs", "nAboveTarget"), ...)
## S4 method for signature 'GeneralSimulationsSummary,missing'
plot(x, y, type = c("nObs", "doseSelected", "propDLTs", "nAboveTarget"), ...)

Arguments

`x`	the `GeneralSimulationsSummary` object we want to plot from
`y`	missing
`type`	the types of plots you want to obtain.
`...`	not used

Details

nObs: Distribution of the number of patients in the simulated trials
doseSelected: Distribution of the final selected doses in the trials. Note that this can include zero entries, meaning that the trial was stopped because all doses in the dose grid appeared too toxic.
propDLTs: Distribution of the proportion of patients with DLTs in the trials
nAboveTarget: Distribution of the number of patients treated at doses which are above the target toxicity interval (as specified by the truth and target arguments to summary,GeneralSimulations-method)

You can specify any subset of these in the type argument.

Value

A single ggplot object if a single plot is asked for, otherwise a gtable object.

Plot for PseudoDualFlexiSimulations

Description

This plot method can be applied to PseudoDualFlexiSimulations objects in order to summarize them graphically. Possible types of plots at the moment are:

trajectory: Summary of the trajectory of the simulated trials
dosesTried: Average proportions of the doses tested in patients
sigma2: The variance of the efficacy responses
sigma2betaW: The variance of the random walk model

You can specify one or both of these in the type argument.

Usage

## S4 method for signature 'PseudoDualFlexiSimulations,missing'
plot(x, y, type = c("trajectory", "dosesTried", "sigma2", "sigma2betaW"), ...)
## S4 method for signature 'PseudoDualFlexiSimulations,missing'
plot(x, y, type = c("trajectory", "dosesTried", "sigma2", "sigma2betaW"), ...)

Arguments

`x`	the `PseudoDualFlexiSimulations` object we want to plot from
`y`	missing
`type`	the type of plots you want to obtain.
`...`	not used

Value

A single ggplot object if a single plot is asked for, otherwise a gtable object.

Examples

##obtain the plot for the simulation results
##If DLE and efficacy responses are considered in the simulations


data <- DataDual(doseGrid=seq(25,300,25))
##First for the DLE model 
##The DLE model must be of 'ModelTox' (e.g 'LogisticIndepBeta') class 
DLEmodel <- LogisticIndepBeta(binDLE=c(1.05,1.8),
                              DLEweights=c(3,3),
                              DLEdose=c(25,300),
                              data=data)

##The efficacy model must be of 'EffFlexi' class

Effmodel<- EffFlexi(Eff=c(1.223, 2.513),Effdose=c(25,300),
                    sigma2=c(a=0.1,b=0.1),sigma2betaW=c(a=20,b=50),smooth="RW2",data=data)

##The escalation rule using the 'NextBestMaxGainSamples' class
mynextbest<-NextBestMaxGainSamples(DLEDuringTrialtarget=0.35,
                                   DLEEndOfTrialtarget=0.3,
                                   TDderive=function(TDsamples){
                                     quantile(TDsamples,prob=0.3)},
                                   Gstarderive=function(Gstarsamples){
                                     quantile(Gstarsamples,prob=0.5)})

## The cohort size, size of 3 subjects
mySize <-CohortSizeConst(size=3)
##Deifne the increments for the dose-escalation process
##The maximum increase of 200% for doses up to the maximum of the dose specified in the doseGrid
##The maximum increase of 200% for dose above the maximum of the dose specified in the doseGrid
##This is to specified a maximum of 3-fold restriction in dose-esclation
myIncrements<-IncrementsRelative(intervals=c(min(data@doseGrid),max(data@doseGrid)), 
                                 increments=c(2,2))
##Specified the stopping rule e.g stop when the maximum sample size of 36 patients has been reached
myStopping <- StoppingMinPatients(nPatients=36)


##Specified the design 
design <- DualResponsesSamplesDesign(nextBest=mynextbest,
                                     cohortSize=mySize,
                                     startingDose=25,
                                     model=DLEmodel,
                                     Effmodel=Effmodel,
                                     data=data,
                                     stopping=myStopping,
                                     increments=myIncrements)
##specified the true DLE curve and the true expected efficacy values at all dose levels
myTruthDLE<- function(dose)
{ DLEmodel@prob(dose, phi1=-53.66584, phi2=10.50499)
}

myTruthEff<- c(-0.5478867, 0.1645417,  0.5248031,  0.7604467,  
               0.9333009  ,1.0687031,  1.1793942 , 1.2726408 , 
               1.3529598 , 1.4233411 , 1.4858613 , 1.5420182)
##The true gain curve can also be seen
myTruthGain <- function(dose)
{return((myTruthEff(dose))/(1+(myTruthDLE(dose)/(1-myTruthDLE(dose)))))}


##options for MCMC
options<-McmcOptions(burnin=10,step=1,samples=20)
##The simulations
##For illustration purpose only 1 simulation is produced (nsim=1). 
mySim<-simulate(object=design,
                args=NULL,
                trueDLE=myTruthDLE,
                trueEff=myTruthEff,
                trueSigma2=0.025,
                trueSigma2betaW=1,
                mcmcOptions=options,
                nsim=1,
                seed=819,
                parallel=FALSE)
##plot this simulated results
print(plot(mySim))
##obtain the plot for the simulation results
##If DLE and efficacy responses are considered in the simulations


data <- DataDual(doseGrid=seq(25,300,25))
##First for the DLE model 
##The DLE model must be of 'ModelTox' (e.g 'LogisticIndepBeta') class 
DLEmodel <- LogisticIndepBeta(binDLE=c(1.05,1.8),
                              DLEweights=c(3,3),
                              DLEdose=c(25,300),
                              data=data)

##The efficacy model must be of 'EffFlexi' class

Effmodel<- EffFlexi(Eff=c(1.223, 2.513),Effdose=c(25,300),
                    sigma2=c(a=0.1,b=0.1),sigma2betaW=c(a=20,b=50),smooth="RW2",data=data)

##The escalation rule using the 'NextBestMaxGainSamples' class
mynextbest<-NextBestMaxGainSamples(DLEDuringTrialtarget=0.35,
                                   DLEEndOfTrialtarget=0.3,
                                   TDderive=function(TDsamples){
                                     quantile(TDsamples,prob=0.3)},
                                   Gstarderive=function(Gstarsamples){
                                     quantile(Gstarsamples,prob=0.5)})

## The cohort size, size of 3 subjects
mySize <-CohortSizeConst(size=3)
##Deifne the increments for the dose-escalation process
##The maximum increase of 200% for doses up to the maximum of the dose specified in the doseGrid
##The maximum increase of 200% for dose above the maximum of the dose specified in the doseGrid
##This is to specified a maximum of 3-fold restriction in dose-esclation
myIncrements<-IncrementsRelative(intervals=c(min(data@doseGrid),max(data@doseGrid)), 
                                 increments=c(2,2))
##Specified the stopping rule e.g stop when the maximum sample size of 36 patients has been reached
myStopping <- StoppingMinPatients(nPatients=36)


##Specified the design 
design <- DualResponsesSamplesDesign(nextBest=mynextbest,
                                     cohortSize=mySize,
                                     startingDose=25,
                                     model=DLEmodel,
                                     Effmodel=Effmodel,
                                     data=data,
                                     stopping=myStopping,
                                     increments=myIncrements)
##specified the true DLE curve and the true expected efficacy values at all dose levels
myTruthDLE<- function(dose)
{ DLEmodel@prob(dose, phi1=-53.66584, phi2=10.50499)
}

myTruthEff<- c(-0.5478867, 0.1645417,  0.5248031,  0.7604467,  
               0.9333009  ,1.0687031,  1.1793942 , 1.2726408 , 
               1.3529598 , 1.4233411 , 1.4858613 , 1.5420182)
##The true gain curve can also be seen
myTruthGain <- function(dose)
{return((myTruthEff(dose))/(1+(myTruthDLE(dose)/(1-myTruthDLE(dose)))))}


##options for MCMC
options<-McmcOptions(burnin=10,step=1,samples=20)
##The simulations
##For illustration purpose only 1 simulation is produced (nsim=1). 
mySim<-simulate(object=design,
                args=NULL,
                trueDLE=myTruthDLE,
                trueEff=myTruthEff,
                trueSigma2=0.025,
                trueSigma2betaW=1,
                mcmcOptions=options,
                nsim=1,
                seed=819,
                parallel=FALSE)
##plot this simulated results
print(plot(mySim))

Plot simulations

Description

Summarize the simulations with plots

Usage

## S4 method for signature 'PseudoDualSimulations,missing'
plot(x, y, type = c("trajectory", "dosesTried", "sigma2"), ...)
## S4 method for signature 'PseudoDualSimulations,missing'
plot(x, y, type = c("trajectory", "dosesTried", "sigma2"), ...)

Arguments

`x`	the `PseudoDualSimulations` object we want to plot from
`y`	missing
`type`	the type of plots you want to obtain.
`...`	not used

Details

This plot method can be applied to PseudoDualSimulations objects in order to summarize them graphically. Possible types of plots at the moment are:

trajectory: Summary of the trajectory of the simulated trials
dosesTried: Average proportions of the doses tested in patients
sigma2: The variance of the efficacy responses

You can specify one or both of these in the type argument.

Value

A single ggplot object if a single plot is asked for, otherwise a gtable object.

Examples

##obtain the plot for the simulation results
##If DLE and efficacy responses are considered in the simulations
##Specified your simulations when no samples are used
## we need a data object with doses >= 1:
data <- DataDual(doseGrid=seq(25,300,25),placebo=FALSE)
##First for the DLE model 
##The DLE model must be of 'ModelTox' (e.g 'LogisticIndepBeta') class 
DLEmodel <- LogisticIndepBeta(binDLE=c(1.05,1.8),
                              DLEweights=c(3,3),
                              DLEdose=c(25,300),
                              data=data)

##The efficacy model of 'ModelEff' (e.g 'Effloglog') class 
Effmodel<-Effloglog(Eff=c(1.223,2.513),Effdose=c(25,300),
                    nu=c(a=1,b=0.025),data=data,c=0)

##The escalation rule using the 'NextBestMaxGain' class
mynextbest<-NextBestMaxGain(DLEDuringTrialtarget=0.35,
                            DLEEndOfTrialtarget=0.3)


##The increments (see Increments class examples) 
## 200% allowable increase for dose below 300 and 200% increase for dose above 300
myIncrements<-IncrementsRelative(intervals=c(25,300),
                                 increments=c(2,2))
##cohort size of 3
mySize<-CohortSizeConst(size=3)
##Stop only when 36 subjects are treated
myStopping <- StoppingMinPatients(nPatients=36)
##Now specified the design with all the above information and starting with a dose of 25

##Specified the design(for details please refer to the 'DualResponsesDesign' example)
design <- DualResponsesDesign(nextBest=mynextbest,
                              model=DLEmodel,
                              Effmodel=Effmodel,
                              stopping=myStopping,
                              increments=myIncrements,
                              cohortSize=mySize,
                              data=data,startingDose=25)
##Specify the true DLE and efficacy curves
myTruthDLE<- function(dose)
{ DLEmodel@prob(dose, phi1=-53.66584, phi2=10.50499)
}

myTruthEff<- function(dose)
{Effmodel@ExpEff(dose,theta1=-4.818429,theta2=3.653058)
}

## Then specified the simulations and generate the trial
##For illustration purpose only 1 simulation is produced (nsim=1). 
mySim <-simulate(object=design,
                 args=NULL,
                 trueDLE=myTruthDLE,
                 trueEff=myTruthEff,
                 trueNu=1/0.025,
                 nsim=1,
                 seed=819,
                 parallel=FALSE)

##plot the simulation results
print(plot(mySim))

##If DLE and efficacy samples are involved
##The escalation rule using the 'NextBestMaxGainSamples' class
mynextbest<-NextBestMaxGainSamples(DLEDuringTrialtarget=0.35,
                                   DLEEndOfTrialtarget=0.3,
                                   TDderive=function(TDsamples){
                                     quantile(TDsamples,prob=0.3)},
                                   Gstarderive=function(Gstarsamples){
                                     quantile(Gstarsamples,prob=0.5)})
##The design of 'DualResponsesSamplesDesign' class
design <- DualResponsesSamplesDesign(nextBest=mynextbest,
                                     cohortSize=mySize,
                                     startingDose=25,
                                     model=DLEmodel,
                                     Effmodel=Effmodel,
                                     data=data,
                                     stopping=myStopping,
                                     increments=myIncrements)
##options for MCMC
options<-McmcOptions(burnin=10,step=1,samples=20)
##The simulations
##For illustration purpose only 1 simulation is produced (nsim=1). 
mySim<-simulate(design,
                args=NULL,
                trueDLE=myTruthDLE,
                trueEff=myTruthEff,
                trueNu=1/0.025,
                nsim=1,
                mcmcOptions=options,
                seed=819,
                parallel=FALSE)
##plot the simulation results
print(plot(mySim))
##obtain the plot for the simulation results
##If DLE and efficacy responses are considered in the simulations
##Specified your simulations when no samples are used
## we need a data object with doses >= 1:
data <- DataDual(doseGrid=seq(25,300,25),placebo=FALSE)
##First for the DLE model 
##The DLE model must be of 'ModelTox' (e.g 'LogisticIndepBeta') class 
DLEmodel <- LogisticIndepBeta(binDLE=c(1.05,1.8),
                              DLEweights=c(3,3),
                              DLEdose=c(25,300),
                              data=data)

##The efficacy model of 'ModelEff' (e.g 'Effloglog') class 
Effmodel<-Effloglog(Eff=c(1.223,2.513),Effdose=c(25,300),
                    nu=c(a=1,b=0.025),data=data,c=0)

##The escalation rule using the 'NextBestMaxGain' class
mynextbest<-NextBestMaxGain(DLEDuringTrialtarget=0.35,
                            DLEEndOfTrialtarget=0.3)


##The increments (see Increments class examples) 
## 200% allowable increase for dose below 300 and 200% increase for dose above 300
myIncrements<-IncrementsRelative(intervals=c(25,300),
                                 increments=c(2,2))
##cohort size of 3
mySize<-CohortSizeConst(size=3)
##Stop only when 36 subjects are treated
myStopping <- StoppingMinPatients(nPatients=36)
##Now specified the design with all the above information and starting with a dose of 25

##Specified the design(for details please refer to the 'DualResponsesDesign' example)
design <- DualResponsesDesign(nextBest=mynextbest,
                              model=DLEmodel,
                              Effmodel=Effmodel,
                              stopping=myStopping,
                              increments=myIncrements,
                              cohortSize=mySize,
                              data=data,startingDose=25)
##Specify the true DLE and efficacy curves
myTruthDLE<- function(dose)
{ DLEmodel@prob(dose, phi1=-53.66584, phi2=10.50499)
}

myTruthEff<- function(dose)
{Effmodel@ExpEff(dose,theta1=-4.818429,theta2=3.653058)
}

## Then specified the simulations and generate the trial
##For illustration purpose only 1 simulation is produced (nsim=1). 
mySim <-simulate(object=design,
                 args=NULL,
                 trueDLE=myTruthDLE,
                 trueEff=myTruthEff,
                 trueNu=1/0.025,
                 nsim=1,
                 seed=819,
                 parallel=FALSE)

##plot the simulation results
print(plot(mySim))

##If DLE and efficacy samples are involved
##The escalation rule using the 'NextBestMaxGainSamples' class
mynextbest<-NextBestMaxGainSamples(DLEDuringTrialtarget=0.35,
                                   DLEEndOfTrialtarget=0.3,
                                   TDderive=function(TDsamples){
                                     quantile(TDsamples,prob=0.3)},
                                   Gstarderive=function(Gstarsamples){
                                     quantile(Gstarsamples,prob=0.5)})
##The design of 'DualResponsesSamplesDesign' class
design <- DualResponsesSamplesDesign(nextBest=mynextbest,
                                     cohortSize=mySize,
                                     startingDose=25,
                                     model=DLEmodel,
                                     Effmodel=Effmodel,
                                     data=data,
                                     stopping=myStopping,
                                     increments=myIncrements)
##options for MCMC
options<-McmcOptions(burnin=10,step=1,samples=20)
##The simulations
##For illustration purpose only 1 simulation is produced (nsim=1). 
mySim<-simulate(design,
                args=NULL,
                trueDLE=myTruthDLE,
                trueEff=myTruthEff,
                trueNu=1/0.025,
                nsim=1,
                mcmcOptions=options,
                seed=819,
                parallel=FALSE)
##plot the simulation results
print(plot(mySim))

Plot the summary of Pseudo Dual Simulations summary

Description

This plot method can be applied to PseudoDualSimulationsSummary objects in order to summarize them graphically. Possible type of plots at the moment are those listed in plot,PseudoSimulationsSummary,missing-method plus:

meanEffFit: Plot showing the fitted dose-efficacy curve. If no samples are involved, only the average fitted dose-efficacy curve across the trials will be ploted. If samples (DLE and efficacy) are involved, the average fitted dose-efficacy curve across the trials, together with the 95% credibility interval; and comparison with the assumed truth (as specified by the trueEff argument to summary,PseudoDualSimulations-method)

You can specify any subset of these in the type argument.

Usage

## S4 method for signature 'PseudoDualSimulationsSummary,missing'
plot(
  x,
  y,
  type = c("nObs", "doseSelected", "propDLE", "nAboveTargetEndOfTrial", "meanFit",
    "meanEffFit"),
  ...
)
## S4 method for signature 'PseudoDualSimulationsSummary,missing'
plot(
  x,
  y,
  type = c("nObs", "doseSelected", "propDLE", "nAboveTargetEndOfTrial", "meanFit",
    "meanEffFit"),
  ...
)

Arguments

`x`	the `PseudoDualSimulationsSummary` object we want to plot from
`y`	missing
`type`	the types of plots you want to obtain.
`...`	not used

Value

A single ggplot object if a single plot is asked for, otherwise a gtable object.

Examples

##obtain the plot of the summary for the simulation results
##If DLE and efficacy responses are considered in the simulations
##Specified your simulations when no samples are used
## we need a data object with doses >= 1:
data <- DataDual(doseGrid=seq(25,300,25),placebo=FALSE)
##First for the DLE model 
##The DLE model must be of 'ModelTox' (e.g 'LogisticIndepBeta') class 
DLEmodel <- LogisticIndepBeta(binDLE=c(1.05,1.8),
                              DLEweights=c(3,3),
                              DLEdose=c(25,300),
                              data=data)

##The efficacy model of 'ModelEff' (e.g 'Effloglog') class 
Effmodel<-Effloglog(Eff=c(1.223,2.513),Effdose=c(25,300),
                    nu=c(a=1,b=0.025),data=data,c=0)

##The escalation rule using the 'NextBestMaxGain' class
mynextbest<-NextBestMaxGain(DLEDuringTrialtarget=0.35,
                            DLEEndOfTrialtarget=0.3)


##The increments (see Increments class examples) 
## 200% allowable increase for dose below 300 and 200% increase for dose above 300
myIncrements<-IncrementsRelative(intervals=c(25,300),
                                 increments=c(2,2))
##cohort size of 3
mySize<-CohortSizeConst(size=3)
##Stop only when 10 subjects are treated (for illustration)
myStopping <- StoppingMinPatients(nPatients=10)
##Now specified the design with all the above information and starting with a dose of 25

##Specified the design(for details please refer to the 'DualResponsesDesign' example)
design <- DualResponsesDesign(nextBest=mynextbest,
                              model=DLEmodel,
                              Effmodel=Effmodel,
                              stopping=myStopping,
                              increments=myIncrements,
                              cohortSize=mySize,
                              data=data,startingDose=25)
##Specify the true DLE and efficacy curves
myTruthDLE<- function(dose)
{ DLEmodel@prob(dose, phi1=-53.66584, phi2=10.50499)
}

myTruthEff<- function(dose)
{Effmodel@ExpEff(dose,theta1=-4.818429,theta2=3.653058)
}

## Then specified the simulations and generate the trial
##For illustration purpose only 1 simulation is produced (nsim=1). 
mySim <-simulate(object=design,
                 args=NULL,
                 trueDLE=myTruthDLE,
                 trueEff=myTruthEff,
                 trueNu=1/0.025,
                 nsim=1,
                 ## this would need to be increased in the real
                 ## application:
                 mcmcOptions=McmcOptions(burnin=10, step=1, samples=50),
                 seed=819,
                 parallel=FALSE)

##Then produce a summary of your simulations
MYSUM <- summary(mySim,
                 trueDLE=myTruthDLE,
                 trueEff=myTruthEff)

##Then plot the summary of the simulations
print(plot(MYSUM))



##If DLE and efficacy samples are involved
##Please refer to design-method 'simulate DualResponsesSamplesDesign' examples for details
##specified the next best
mynextbest<-NextBestMaxGainSamples(DLEDuringTrialtarget=0.35,
                                   DLEEndOfTrialtarget=0.3,
                                   TDderive=function(TDsamples){
                                     quantile(TDsamples,prob=0.3)},
                                   Gstarderive=function(Gstarsamples){
                                     quantile(Gstarsamples,prob=0.5)})
##specified the design
design <- DualResponsesSamplesDesign(nextBest=mynextbest,
                                     cohortSize=mySize,
                                     startingDose=25,
                                     model=DLEmodel,
                                     Effmodel=Effmodel,
                                     data=data,
                                     stopping=myStopping,
                                     increments=myIncrements)
##options for MCMC
##for illustration purpose we use 10 burn-in and generate 50 samples
options<-McmcOptions(burnin=10,step=2,samples=50)
##The simulations
##For illustration purpose only 1 simulation is produced (nsim=1). 
# mySim<-simulate(design,
#                 args=NULL,
#                 trueDLE=myTruthDLE,
#                 trueEff=myTruthEff,
#                 trueNu=1/0.025,
#                 nsim=1,
#                 mcmcOptions=options,
#                 seed=819,
#                 parallel=FALSE)
# 
# ##Then produce a summary of your simulations
# MYSUM <- summary(mySim,
#                  trueDLE=myTruthDLE,
#                  trueEff=myTruthEff)
# 
# ##Then plot the summary of the simulations
# print(plot(MYSUM))



##OR if the 'EffFlexi' class is used 
## for the efficacy model

Effmodel<- EffFlexi(Eff=c(1.223, 2.513),Effdose=c(25,300),
                    sigma2=c(a=0.1,b=0.1),sigma2betaW=c(a=20,b=50),smooth="RW2",data=data)

##Specified the design 
design <- DualResponsesSamplesDesign(nextBest=mynextbest,
                                     cohortSize=mySize,
                                     startingDose=25,
                                     model=DLEmodel,
                                     Effmodel=Effmodel,
                                     data=data,
                                     stopping=myStopping,
                                     increments=myIncrements)
##specified the true DLE curve and the true expected efficacy values at all dose levels
myTruthDLE<- function(dose)
{ DLEmodel@prob(dose, phi1=-53.66584, phi2=10.50499)
}

myTruthEff<- c(-0.5478867, 0.1645417,  0.5248031,  0.7604467,  
               0.9333009  ,1.0687031,  1.1793942 , 1.2726408 , 
               1.3529598 , 1.4233411 , 1.4858613 , 1.5420182)
##The true gain curve can also be seen
myTruthGain <- function(dose)
{return((myTruthEff(dose))/(1+(myTruthDLE(dose)/(1-myTruthDLE(dose)))))}

##The simulations
# ##For illustration purpose only 1 simulation is produced (nsim=1). 
# mySim<-simulate(object=design,
#                 args=NULL,
#                 trueDLE=myTruthDLE,
#                 trueEff=myTruthEff,
#                 trueSigma2=0.025,
#                 trueSigma2betaW=1,
#                 nsim=1,
#                 mcmcOptions=options,
#                 seed=819,
#                 parallel=FALSE)
# ##Then produce a summary of your simulations
# MYSUM <- summary(mySim,
#                  trueDLE=myTruthDLE,
#                  trueEff=myTruthEff)
# 
# ##Then plot the summary of the simulations
# print(plot(MYSUM))
##obtain the plot of the summary for the simulation results
##If DLE and efficacy responses are considered in the simulations
##Specified your simulations when no samples are used
## we need a data object with doses >= 1:
data <- DataDual(doseGrid=seq(25,300,25),placebo=FALSE)
##First for the DLE model 
##The DLE model must be of 'ModelTox' (e.g 'LogisticIndepBeta') class 
DLEmodel <- LogisticIndepBeta(binDLE=c(1.05,1.8),
                              DLEweights=c(3,3),
                              DLEdose=c(25,300),
                              data=data)

##The efficacy model of 'ModelEff' (e.g 'Effloglog') class 
Effmodel<-Effloglog(Eff=c(1.223,2.513),Effdose=c(25,300),
                    nu=c(a=1,b=0.025),data=data,c=0)

##The escalation rule using the 'NextBestMaxGain' class
mynextbest<-NextBestMaxGain(DLEDuringTrialtarget=0.35,
                            DLEEndOfTrialtarget=0.3)


##The increments (see Increments class examples) 
## 200% allowable increase for dose below 300 and 200% increase for dose above 300
myIncrements<-IncrementsRelative(intervals=c(25,300),
                                 increments=c(2,2))
##cohort size of 3
mySize<-CohortSizeConst(size=3)
##Stop only when 10 subjects are treated (for illustration)
myStopping <- StoppingMinPatients(nPatients=10)
##Now specified the design with all the above information and starting with a dose of 25

##Specified the design(for details please refer to the 'DualResponsesDesign' example)
design <- DualResponsesDesign(nextBest=mynextbest,
                              model=DLEmodel,
                              Effmodel=Effmodel,
                              stopping=myStopping,
                              increments=myIncrements,
                              cohortSize=mySize,
                              data=data,startingDose=25)
##Specify the true DLE and efficacy curves
myTruthDLE<- function(dose)
{ DLEmodel@prob(dose, phi1=-53.66584, phi2=10.50499)
}

myTruthEff<- function(dose)
{Effmodel@ExpEff(dose,theta1=-4.818429,theta2=3.653058)
}

## Then specified the simulations and generate the trial
##For illustration purpose only 1 simulation is produced (nsim=1). 
mySim <-simulate(object=design,
                 args=NULL,
                 trueDLE=myTruthDLE,
                 trueEff=myTruthEff,
                 trueNu=1/0.025,
                 nsim=1,
                 ## this would need to be increased in the real
                 ## application:
                 mcmcOptions=McmcOptions(burnin=10, step=1, samples=50),
                 seed=819,
                 parallel=FALSE)

##Then produce a summary of your simulations
MYSUM <- summary(mySim,
                 trueDLE=myTruthDLE,
                 trueEff=myTruthEff)

##Then plot the summary of the simulations
print(plot(MYSUM))



##If DLE and efficacy samples are involved
##Please refer to design-method 'simulate DualResponsesSamplesDesign' examples for details
##specified the next best
mynextbest<-NextBestMaxGainSamples(DLEDuringTrialtarget=0.35,
                                   DLEEndOfTrialtarget=0.3,
                                   TDderive=function(TDsamples){
                                     quantile(TDsamples,prob=0.3)},
                                   Gstarderive=function(Gstarsamples){
                                     quantile(Gstarsamples,prob=0.5)})
##specified the design
design <- DualResponsesSamplesDesign(nextBest=mynextbest,
                                     cohortSize=mySize,
                                     startingDose=25,
                                     model=DLEmodel,
                                     Effmodel=Effmodel,
                                     data=data,
                                     stopping=myStopping,
                                     increments=myIncrements)
##options for MCMC
##for illustration purpose we use 10 burn-in and generate 50 samples
options<-McmcOptions(burnin=10,step=2,samples=50)
##The simulations
##For illustration purpose only 1 simulation is produced (nsim=1). 
# mySim<-simulate(design,
#                 args=NULL,
#                 trueDLE=myTruthDLE,
#                 trueEff=myTruthEff,
#                 trueNu=1/0.025,
#                 nsim=1,
#                 mcmcOptions=options,
#                 seed=819,
#                 parallel=FALSE)
# 
# ##Then produce a summary of your simulations
# MYSUM <- summary(mySim,
#                  trueDLE=myTruthDLE,
#                  trueEff=myTruthEff)
# 
# ##Then plot the summary of the simulations
# print(plot(MYSUM))



##OR if the 'EffFlexi' class is used 
## for the efficacy model

Effmodel<- EffFlexi(Eff=c(1.223, 2.513),Effdose=c(25,300),
                    sigma2=c(a=0.1,b=0.1),sigma2betaW=c(a=20,b=50),smooth="RW2",data=data)

##Specified the design 
design <- DualResponsesSamplesDesign(nextBest=mynextbest,
                                     cohortSize=mySize,
                                     startingDose=25,
                                     model=DLEmodel,
                                     Effmodel=Effmodel,
                                     data=data,
                                     stopping=myStopping,
                                     increments=myIncrements)
##specified the true DLE curve and the true expected efficacy values at all dose levels
myTruthDLE<- function(dose)
{ DLEmodel@prob(dose, phi1=-53.66584, phi2=10.50499)
}

myTruthEff<- c(-0.5478867, 0.1645417,  0.5248031,  0.7604467,  
               0.9333009  ,1.0687031,  1.1793942 , 1.2726408 , 
               1.3529598 , 1.4233411 , 1.4858613 , 1.5420182)
##The true gain curve can also be seen
myTruthGain <- function(dose)
{return((myTruthEff(dose))/(1+(myTruthDLE(dose)/(1-myTruthDLE(dose)))))}

##The simulations
# ##For illustration purpose only 1 simulation is produced (nsim=1). 
# mySim<-simulate(object=design,
#                 args=NULL,
#                 trueDLE=myTruthDLE,
#                 trueEff=myTruthEff,
#                 trueSigma2=0.025,
#                 trueSigma2betaW=1,
#                 nsim=1,
#                 mcmcOptions=options,
#                 seed=819,
#                 parallel=FALSE)
# ##Then produce a summary of your simulations
# MYSUM <- summary(mySim,
#                  trueDLE=myTruthDLE,
#                  trueEff=myTruthEff)
# 
# ##Then plot the summary of the simulations
# print(plot(MYSUM))

Plot summaries of the pseudo simulations

Description

Graphical display of the simulation summary

Usage

## S4 method for signature 'PseudoSimulationsSummary,missing'
plot(
  x,
  y,
  type = c("nObs", "doseSelected", "propDLE", "nAboveTargetEndOfTrial", "meanFit"),
  ...
)
## S4 method for signature 'PseudoSimulationsSummary,missing'
plot(
  x,
  y,
  type = c("nObs", "doseSelected", "propDLE", "nAboveTargetEndOfTrial", "meanFit"),
  ...
)

Arguments

`x`	the `PseudoSimulationsSummary` object we want to plot from
`y`	missing
`type`	the types of plots you want to obtain.
`...`	not used

Details

This plot method can be applied to PseudoSimulationsSummary objects in order to summarize them graphically. This can be used when only DLE responses are involved in the simulations. This also applied to results with or without samples generated during the simulations

Value

A single ggplot object if a single plot is asked for, otherwise a gtable object.

Examples

##obtain the plot for the simulation results
##If only DLE responses are considered in the simulations
##Specified your simulations when no DLE samples are used
##Define your data set first using an empty data set 
## with dose levels from 25 to 300 with increments 25
data <- Data(doseGrid=seq(25,300,25))

##Specified the model of 'ModelTox' class eg 'LogisticIndepBeta' class model
model<-LogisticIndepBeta(binDLE=c(1.05,1.8),DLEweights=c(3,3),DLEdose=c(25,300),data=data)
##Then the escalation rule
tdNextBest <- NextBestTD(targetDuringTrial=0.35,
                         targetEndOfTrial=0.3)

## The cohort size, size of 3 subjects
mySize <-CohortSizeConst(size=3)
##Deifne the increments for the dose-escalation process
##The maximum increase of 200% for doses up to the maximum of the dose specified in the doseGrid
##The maximum increase of 200% for dose above the maximum of the dose specified in the doseGrid
##This is to specified a maximum of 3-fold restriction in dose-esclation
myIncrements<-IncrementsRelative(intervals=c(min(data@doseGrid),max(data@doseGrid)), 
                                 increments=c(2,2))
##Specified the stopping rule e.g stop when the maximum sample size of 12 patients has been reached
myStopping <- StoppingMinPatients(nPatients=12)
##Now specified the design with all the above information and starting with a dose of 25
design <- TDDesign(model=model,
                   nextBest=tdNextBest,
                   stopping=myStopping,
                   increments=myIncrements,
                   cohortSize=mySize,
                  data=data,startingDose=25)

##Specify the truth of the DLE responses
myTruth <- function(dose)
{ model@prob(dose, phi1=-53.66584, phi2=10.50499)
}

## Then specified the simulations and generate the trial 
##For illustration purpose only 1 simulation is produced (nsim=1). 
##The simulations
mySim <- simulate(design,
                  args=NULL,
                  truth=myTruth,
                  nsim=1,
                  seed=819,
                  parallel=FALSE)

##Then produce a summary of your simulations
MYSUM <- summary(mySim,
                 truth=myTruth)
##plot the summary of the simulations
print(plot(MYSUM))



##If DLE samples are involved
##The escalation rule
tdNextBest<-NextBestTDsamples(targetDuringTrial=0.35,
                              targetEndOfTrial=0.3,
                              derive=function(TDsamples){quantile(TDsamples,probs=0.3)})
##specify the design
design <- TDsamplesDesign(model=model,
                          nextBest=tdNextBest,
                          stopping=myStopping,
                          increments=myIncrements,
                          cohortSize=mySize,
                          data=data,startingDose=25)
##options for MCMC
options<-McmcOptions(burnin=100,step=2,samples=200)
##The simulations
##For illustration purpose only 1 simulation is produced (nsim=1). 
# mySim <- simulate(design,
#                   args=NULL,
#                   truth=myTruth,
#                   nsim=1,
#                   seed=819,
#                   mcmcOptions=options,
#                   parallel=FALSE)
# ##Then produce a summary of your simulations
# MYSUM <- summary(mySim,
#                  truth=myTruth)
# ##plot the summary of the simulations
# print(plot(MYSUM))
##obtain the plot for the simulation results
##If only DLE responses are considered in the simulations
##Specified your simulations when no DLE samples are used
##Define your data set first using an empty data set 
## with dose levels from 25 to 300 with increments 25
data <- Data(doseGrid=seq(25,300,25))

##Specified the model of 'ModelTox' class eg 'LogisticIndepBeta' class model
model<-LogisticIndepBeta(binDLE=c(1.05,1.8),DLEweights=c(3,3),DLEdose=c(25,300),data=data)
##Then the escalation rule
tdNextBest <- NextBestTD(targetDuringTrial=0.35,
                         targetEndOfTrial=0.3)

## The cohort size, size of 3 subjects
mySize <-CohortSizeConst(size=3)
##Deifne the increments for the dose-escalation process
##The maximum increase of 200% for doses up to the maximum of the dose specified in the doseGrid
##The maximum increase of 200% for dose above the maximum of the dose specified in the doseGrid
##This is to specified a maximum of 3-fold restriction in dose-esclation
myIncrements<-IncrementsRelative(intervals=c(min(data@doseGrid),max(data@doseGrid)), 
                                 increments=c(2,2))
##Specified the stopping rule e.g stop when the maximum sample size of 12 patients has been reached
myStopping <- StoppingMinPatients(nPatients=12)
##Now specified the design with all the above information and starting with a dose of 25
design <- TDDesign(model=model,
                   nextBest=tdNextBest,
                   stopping=myStopping,
                   increments=myIncrements,
                   cohortSize=mySize,
                  data=data,startingDose=25)

##Specify the truth of the DLE responses
myTruth <- function(dose)
{ model@prob(dose, phi1=-53.66584, phi2=10.50499)
}

## Then specified the simulations and generate the trial 
##For illustration purpose only 1 simulation is produced (nsim=1). 
##The simulations
mySim <- simulate(design,
                  args=NULL,
                  truth=myTruth,
                  nsim=1,
                  seed=819,
                  parallel=FALSE)

##Then produce a summary of your simulations
MYSUM <- summary(mySim,
                 truth=myTruth)
##plot the summary of the simulations
print(plot(MYSUM))



##If DLE samples are involved
##The escalation rule
tdNextBest<-NextBestTDsamples(targetDuringTrial=0.35,
                              targetEndOfTrial=0.3,
                              derive=function(TDsamples){quantile(TDsamples,probs=0.3)})
##specify the design
design <- TDsamplesDesign(model=model,
                          nextBest=tdNextBest,
                          stopping=myStopping,
                          increments=myIncrements,
                          cohortSize=mySize,
                          data=data,startingDose=25)
##options for MCMC
options<-McmcOptions(burnin=100,step=2,samples=200)
##The simulations
##For illustration purpose only 1 simulation is produced (nsim=1). 
# mySim <- simulate(design,
#                   args=NULL,
#                   truth=myTruth,
#                   nsim=1,
#                   seed=819,
#                   mcmcOptions=options,
#                   parallel=FALSE)
# ##Then produce a summary of your simulations
# MYSUM <- summary(mySim,
#                  truth=myTruth)
# ##plot the summary of the simulations
# print(plot(MYSUM))

Plotting dose-toxicity and dose-biomarker model fits

Description

When we have the dual endpoint model, also the dose-biomarker fit is shown in the plot

Usage

## S4 method for signature 'Samples,DualEndpoint'
plot(x, y, data, extrapolate = TRUE, showLegend = FALSE, ...)
## S4 method for signature 'Samples,DualEndpoint'
plot(x, y, data, extrapolate = TRUE, showLegend = FALSE, ...)

Arguments

`x`	the `Samples` object
`y`	the `DualEndpoint` object
`data`	the `DataDual` object
`extrapolate`	should the biomarker fit be extrapolated to the whole dose grid? (default)
`showLegend`	should the legend be shown? (not default)
`...`	additional arguments for the parent method `plot,Samples,Model-method`

Value

This returns the ggplot object with the dose-toxicity and dose-biomarker model fits

Examples


# Create some data
data <- DataDual(
  x=c(0.1, 0.5, 1.5, 3, 6, 10, 10, 10,
      20, 20, 20, 40, 40, 40, 50, 50, 50),
  y=c(0, 0, 0, 0, 0, 0, 1, 0,
      0, 1, 1, 0, 0, 1, 0, 1, 1),
  w=c(0.31, 0.42, 0.59, 0.45, 0.6, 0.7, 0.55, 0.6,
      0.52, 0.54, 0.56, 0.43, 0.41, 0.39, 0.34, 0.38, 0.21),
  doseGrid=c(0.1, 0.5, 1.5, 3, 6,
             seq(from=10, to=80, by=2)))

# Initialize the Dual-Endpoint model (in this case RW1)
model <- DualEndpointRW(mu = c(0, 1),
                        Sigma = matrix(c(1, 0, 0, 1), nrow=2),
                        sigma2betaW = 0.01,
                        sigma2W = c(a=0.1, b=0.1),
                        rho = c(a=1, b=1),
                        smooth = "RW1")

# Set-up some MCMC parameters and generate samples from the posterior
options <- McmcOptions(burnin=100,
                       step=2,
                       samples=500)
set.seed(94)
samples <- mcmc(data, model, options)

# Plot the posterior mean  (and empirical 2.5 and 97.5 percentile)
# for the prob(DLT) by doses and the Biomarker by doses
#grid.arrange(plot(x = samples, y = model, data = data))
              
plot(x = samples, y = model, data = data)


# Create some data
data <- DataDual(
  x=c(0.1, 0.5, 1.5, 3, 6, 10, 10, 10,
      20, 20, 20, 40, 40, 40, 50, 50, 50),
  y=c(0, 0, 0, 0, 0, 0, 1, 0,
      0, 1, 1, 0, 0, 1, 0, 1, 1),
  w=c(0.31, 0.42, 0.59, 0.45, 0.6, 0.7, 0.55, 0.6,
      0.52, 0.54, 0.56, 0.43, 0.41, 0.39, 0.34, 0.38, 0.21),
  doseGrid=c(0.1, 0.5, 1.5, 3, 6,
             seq(from=10, to=80, by=2)))

# Initialize the Dual-Endpoint model (in this case RW1)
model <- DualEndpointRW(mu = c(0, 1),
                        Sigma = matrix(c(1, 0, 0, 1), nrow=2),
                        sigma2betaW = 0.01,
                        sigma2W = c(a=0.1, b=0.1),
                        rho = c(a=1, b=1),
                        smooth = "RW1")

# Set-up some MCMC parameters and generate samples from the posterior
options <- McmcOptions(burnin=100,
                       step=2,
                       samples=500)
set.seed(94)
samples <- mcmc(data, model, options)

# Plot the posterior mean  (and empirical 2.5 and 97.5 percentile)
# for the prob(DLT) by doses and the Biomarker by doses
#grid.arrange(plot(x = samples, y = model, data = data))
              
plot(x = samples, y = model, data = data)

Plotting dose-toxicity model fits

Description

Plotting dose-toxicity model fits

Usage

## S4 method for signature 'Samples,Model'
plot(
  x,
  y,
  data,
  ...,
  xlab = "Dose level",
  ylab = "Probability of DLT [%]",
  showLegend = TRUE
)
## S4 method for signature 'Samples,Model'
plot(
  x,
  y,
  data,
  ...,
  xlab = "Dose level",
  ylab = "Probability of DLT [%]",
  showLegend = TRUE
)

Arguments

`x`	the `Samples` object
`y`	the `Model` object
`data`	the `Data` object
`...`	not used
`xlab`	the x axis label
`ylab`	the y axis label
`showLegend`	should the legend be shown? (default)

Value

This returns the ggplot object for the dose-toxicity model fit

Examples


# Create some data
data <- Data(x = c(0.1, 0.5, 1.5, 3, 6, 10, 10, 10),
             y = c(0, 0, 0, 0, 0, 0, 1, 0),
             cohort = c(0, 1, 2, 3, 4, 5, 5, 5),
             doseGrid = c(0.1, 0.5, 1.5, 3, 6,
                          seq(from = 10, to = 80, by=2)))

# Initialize a model 
model <- LogisticLogNormal(mean = c(-0.85, 1),
                           cov = matrix(c(1, -0.5, -0.5, 1), nrow = 2),
                           refDose = 56)

# Get posterior for all model parameters
options <- McmcOptions(burnin = 100,
                       step = 2,
                       samples = 2000)
set.seed(94)
samples <- mcmc(data, model, options)

# Plot the posterior mean  (and empirical 2.5 and 97.5 percentile)
# for the prob(DLT) by doses
plot(x = samples, y = model, data = data)
              



# Create some data
data <- Data(x = c(0.1, 0.5, 1.5, 3, 6, 10, 10, 10),
             y = c(0, 0, 0, 0, 0, 0, 1, 0),
             cohort = c(0, 1, 2, 3, 4, 5, 5, 5),
             doseGrid = c(0.1, 0.5, 1.5, 3, 6,
                          seq(from = 10, to = 80, by=2)))

# Initialize a model 
model <- LogisticLogNormal(mean = c(-0.85, 1),
                           cov = matrix(c(1, -0.5, -0.5, 1), nrow = 2),
                           refDose = 56)

# Get posterior for all model parameters
options <- McmcOptions(burnin = 100,
                       step = 2,
                       samples = 2000)
set.seed(94)
samples <- mcmc(data, model, options)

# Plot the posterior mean  (and empirical 2.5 and 97.5 percentile)
# for the prob(DLT) by doses
plot(x = samples, y = model, data = data)

Plot the fitted dose-effcacy curve using a model from `ModelEff` class with samples

Description

Plot the fitted dose-effcacy curve using a model from ModelEff class with samples

Usage

## S4 method for signature 'Samples,ModelEff'
plot(
  x,
  y,
  data,
  ...,
  xlab = "Dose level",
  ylab = "Expected Efficacy",
  showLegend = TRUE
)
## S4 method for signature 'Samples,ModelEff'
plot(
  x,
  y,
  data,
  ...,
  xlab = "Dose level",
  ylab = "Expected Efficacy",
  showLegend = TRUE
)

Arguments

`x`	the `Samples` object
`y`	the `ModelEff` model class object
`data`	the `Data` object
`...`	not used
`xlab`	the x axis label
`ylab`	the y axis label
`showLegend`	should the legend be shown? (default)

Value

This returns the ggplot object for the dose-efficacy model fit

Examples

## we need a data object with doses >= 1:
data <-DataDual(x=c(25,50,25,50,75,300,250,150),
                y=c(0,0,0,0,0,1,1,0),
                w=c(0.31,0.42,0.59,0.45,0.6,0.7,0.6,0.52),
                doseGrid=seq(25,300,25),
                placebo=FALSE)
##plot the dose-efficacy curve with samples using the model from 'ModelEff' 
##class e.g. 'Effloglog' class model
##define the model (see Effloglog example)
Effmodel<-Effloglog(Eff=c(1.223,2.513),Effdose=c(25,300),nu=c(a=1,b=0.025),data=data,c=0)
## define the samples obtained using the 'Effloglog' model (see details in 'Samples' example)
##options for MCMC
options<-McmcOptions(burnin=100,step=2,samples=200)
## samples must be of 'Samples' class
samples <- mcmc(data=data,model=Effmodel,options=options)
## plot the fitted dose-efficacy curve including the 95% credibility interval of the samples
## 'x' should be of 'Samples' class and 'y' of 'ModelEff' class
plot(x=samples,y=Effmodel,data=data)
## we need a data object with doses >= 1:
data <-DataDual(x=c(25,50,25,50,75,300,250,150),
                y=c(0,0,0,0,0,1,1,0),
                w=c(0.31,0.42,0.59,0.45,0.6,0.7,0.6,0.52),
                doseGrid=seq(25,300,25),
                placebo=FALSE)
##plot the dose-efficacy curve with samples using the model from 'ModelEff' 
##class e.g. 'Effloglog' class model
##define the model (see Effloglog example)
Effmodel<-Effloglog(Eff=c(1.223,2.513),Effdose=c(25,300),nu=c(a=1,b=0.025),data=data,c=0)
## define the samples obtained using the 'Effloglog' model (see details in 'Samples' example)
##options for MCMC
options<-McmcOptions(burnin=100,step=2,samples=200)
## samples must be of 'Samples' class
samples <- mcmc(data=data,model=Effmodel,options=options)
## plot the fitted dose-efficacy curve including the 95% credibility interval of the samples
## 'x' should be of 'Samples' class and 'y' of 'ModelEff' class
plot(x=samples,y=Effmodel,data=data)

Plot the fitted dose-DLE curve using a `ModelTox` class model with samples

Description

Plot the fitted dose-DLE curve using a ModelTox class model with samples

Usage

## S4 method for signature 'Samples,ModelTox'
plot(
  x,
  y,
  data,
  ...,
  xlab = "Dose level",
  ylab = "Probability of DLT [%]",
  showLegend = TRUE
)
## S4 method for signature 'Samples,ModelTox'
plot(
  x,
  y,
  data,
  ...,
  xlab = "Dose level",
  ylab = "Probability of DLT [%]",
  showLegend = TRUE
)

Arguments

`x`	the `Samples` object
`y`	the `ModelTox` model class object
`data`	the `Data` object
`...`	not used
`xlab`	the x axis label
`ylab`	the y axis label
`showLegend`	should the legend be shown? (default)

Value

This returns the ggplot object for the dose-DLE model fit

Examples

## we need a data object with doses >= 1:
data<-Data(x=c(25,50,50,75,150,200,225,300),
           y=c(0,0,0,0,1,1,1,1),
           doseGrid=seq(from=25,to=300,by=25))
##plot the dose-DLE curve with samples using the model from 'ModelTox' 
##class e.g. 'LogisticIndepBeta' class model
##define the model (see LogisticIndepBeta example)
model <-LogisticIndepBeta(binDLE=c(1.05,1.8),DLEweights=c(3,3),DLEdose=c(25,300),data=data)
## define the samples obtained using the 'LogisticIndepGBeta' model 

##Define options for MCMC
options<-McmcOptions(burnin=100,step=2,samples=200)
## (see details in 'Samples' example) samples must be of 'Samples' class
samples <- mcmc(data=data,model=model,options=options)
## plot the fitted dose-DLE curve including the 95% credibility interval of the samples
## 'x' should be of 'Samples' class and 'y' of 'ModelTox' class
plot(x=samples,y=model,data=data)
## we need a data object with doses >= 1:
data<-Data(x=c(25,50,50,75,150,200,225,300),
           y=c(0,0,0,0,1,1,1,1),
           doseGrid=seq(from=25,to=300,by=25))
##plot the dose-DLE curve with samples using the model from 'ModelTox' 
##class e.g. 'LogisticIndepBeta' class model
##define the model (see LogisticIndepBeta example)
model <-LogisticIndepBeta(binDLE=c(1.05,1.8),DLEweights=c(3,3),DLEdose=c(25,300),data=data)
## define the samples obtained using the 'LogisticIndepGBeta' model 

##Define options for MCMC
options<-McmcOptions(burnin=100,step=2,samples=200)
## (see details in 'Samples' example) samples must be of 'Samples' class
samples <- mcmc(data=data,model=model,options=options)
## plot the fitted dose-DLE curve including the 95% credibility interval of the samples
## 'x' should be of 'Samples' class and 'y' of 'ModelTox' class
plot(x=samples,y=model,data=data)

Plot summaries of the model-based design simulations

Description

Graphical display of the simulation summary

Usage

## S4 method for signature 'SimulationsSummary,missing'
plot(
  x,
  y,
  type = c("nObs", "doseSelected", "propDLTs", "nAboveTarget", "meanFit"),
  ...
)
## S4 method for signature 'SimulationsSummary,missing'
plot(
  x,
  y,
  type = c("nObs", "doseSelected", "propDLTs", "nAboveTarget", "meanFit"),
  ...
)

Arguments

`x`	the `SimulationsSummary` object we want to plot from
`y`	missing
`type`	the types of plots you want to obtain.
`...`	not used

Details

This plot method can be applied to SimulationsSummary objects in order to summarize them graphically. Possible type of plots at the moment are those listed in plot,GeneralSimulationsSummary,missing-method plus:

meanFit: Plot showing the average fitted dose-toxicity curve across the trials, together with 95% credible intervals, and comparison with the assumed truth (as specified by the truth argument to summary,Simulations-method)

You can specify any subset of these in the type argument.

Value

A single ggplot object if a single plot is asked for, otherwise a gtable object.

Examples


# Define the dose-grid
emptydata <- Data(doseGrid = c(1, 3, 5, 10, 15, 20, 25, 40, 50, 80, 100))

# Initialize the CRM model 
model <- LogisticLogNormal(mean=c(-0.85, 1),
                           cov=
                             matrix(c(1, -0.5, -0.5, 1),
                                    nrow=2),
                           refDose=56)

# Choose the rule for selecting the next dose 
myNextBest <- NextBestNCRM(target=c(0.2, 0.35),
                           overdose=c(0.35, 1),
                           maxOverdoseProb=0.25)

# Choose the rule for the cohort-size 
mySize1 <- CohortSizeRange(intervals=c(0, 30),
                           cohortSize=c(1, 3))
mySize2 <- CohortSizeDLT(DLTintervals=c(0, 1),
                         cohortSize=c(1, 3))
mySize <- maxSize(mySize1, mySize2)

# Choose the rule for stopping
myStopping1 <- StoppingMinCohorts(nCohorts=3)
myStopping2 <- StoppingTargetProb(target=c(0.2, 0.35),
                                  prob=0.5)
myStopping3 <- StoppingMinPatients(nPatients=20)
myStopping <- (myStopping1 & myStopping2) | myStopping3

# Choose the rule for dose increments
myIncrements <- IncrementsRelative(intervals=c(0, 20),
                                   increments=c(1, 0.33))

# Initialize the design
design <- Design(model=model,
                 nextBest=myNextBest,
                 stopping=myStopping,
                 increments=myIncrements,
                 cohortSize=mySize,
                 data=emptydata,
                 startingDose=3)

## define the true function
myTruth <- function(dose)
{
  model@prob(dose, alpha0=7, alpha1=8)
}

# Run the simulation on the desired design
# We only generate 1 trial outcomes here for illustration, for the actual study 
# this should be increased of course
options <- McmcOptions(burnin=10,
                       step=1,
                       samples=100)
time <- system.time(mySims <- simulate(design,
                                       args=NULL,
                                       truth=myTruth,
                                       nsim=1,
                                       seed=819,
                                       mcmcOptions=options,
                                       parallel=FALSE))[3]

# Plot the Summary of the Simulations
plot(summary(mySims,truth=myTruth))


# Define the dose-grid
emptydata <- Data(doseGrid = c(1, 3, 5, 10, 15, 20, 25, 40, 50, 80, 100))

# Initialize the CRM model 
model <- LogisticLogNormal(mean=c(-0.85, 1),
                           cov=
                             matrix(c(1, -0.5, -0.5, 1),
                                    nrow=2),
                           refDose=56)

# Choose the rule for selecting the next dose 
myNextBest <- NextBestNCRM(target=c(0.2, 0.35),
                           overdose=c(0.35, 1),
                           maxOverdoseProb=0.25)

# Choose the rule for the cohort-size 
mySize1 <- CohortSizeRange(intervals=c(0, 30),
                           cohortSize=c(1, 3))
mySize2 <- CohortSizeDLT(DLTintervals=c(0, 1),
                         cohortSize=c(1, 3))
mySize <- maxSize(mySize1, mySize2)

# Choose the rule for stopping
myStopping1 <- StoppingMinCohorts(nCohorts=3)
myStopping2 <- StoppingTargetProb(target=c(0.2, 0.35),
                                  prob=0.5)
myStopping3 <- StoppingMinPatients(nPatients=20)
myStopping <- (myStopping1 & myStopping2) | myStopping3

# Choose the rule for dose increments
myIncrements <- IncrementsRelative(intervals=c(0, 20),
                                   increments=c(1, 0.33))

# Initialize the design
design <- Design(model=model,
                 nextBest=myNextBest,
                 stopping=myStopping,
                 increments=myIncrements,
                 cohortSize=mySize,
                 data=emptydata,
                 startingDose=3)

## define the true function
myTruth <- function(dose)
{
  model@prob(dose, alpha0=7, alpha1=8)
}

# Run the simulation on the desired design
# We only generate 1 trial outcomes here for illustration, for the actual study 
# this should be increased of course
options <- McmcOptions(burnin=10,
                       step=1,
                       samples=100)
time <- system.time(mySims <- simulate(design,
                                       args=NULL,
                                       truth=myTruth,
                                       nsim=1,
                                       seed=819,
                                       mcmcOptions=options,
                                       parallel=FALSE))[3]

# Plot the Summary of the Simulations
plot(summary(mySims,truth=myTruth))

Plots gtable objects

Description

Plots gtable objects

Usage

## S3 method for class 'gtable'
plot(x, ...)
## S3 method for class 'gtable'
plot(x, ...)

Arguments

`x`	the gtable object
`...`	additional parameters for `grid.draw`

Plot of the DLE and efficacy curve side by side given a DLE pseudo model, a DLE sample, an efficacy pseudo model and a given efficacy sample

Description

Plot of the DLE and efficacy curve side by side given a DLE pseudo model, a DLE sample, an efficacy pseudo model and a given efficacy sample

Plot of the dose-DLE and dose-efficacy curve side by side given a DLE pseudo model and a given pseudo efficacy model without DLE and efficacy samples

Usage

plotDualResponses(DLEmodel, DLEsamples, Effmodel, Effsamples, data, ...)

## S4 method for signature 'ModelTox,Samples,ModelEff,Samples'
plotDualResponses(
  DLEmodel,
  DLEsamples,
  Effmodel,
  Effsamples,
  data,
  extrapolate = TRUE,
  showLegend = FALSE,
  ...
)

## S4 method for signature 'ModelTox,missing,ModelEff,missing'
plotDualResponses(DLEmodel, DLEsamples, Effmodel, Effsamples, data, ...)
plotDualResponses(DLEmodel, DLEsamples, Effmodel, Effsamples, data, ...)

## S4 method for signature 'ModelTox,Samples,ModelEff,Samples'
plotDualResponses(
  DLEmodel,
  DLEsamples,
  Effmodel,
  Effsamples,
  data,
  extrapolate = TRUE,
  showLegend = FALSE,
  ...
)

## S4 method for signature 'ModelTox,missing,ModelEff,missing'
plotDualResponses(DLEmodel, DLEsamples, Effmodel, Effsamples, data, ...)

Arguments

`DLEmodel`	the pseudo DLE model of `ModelTox` class object
`DLEsamples`	the DLE samples of `Samples` class object
`Effmodel`	the pseudo efficacy model of `ModelEff` class object
`Effsamples`	the Efficacy samples of `Samples` class object
`data`	the data input of `DataDual` class object
`...`	additional arguments for the parent method `plot,Samples,Model-method`
`extrapolate`	should the biomarker fit be extrapolated to the whole dose grid? (default)
`showLegend`	should the legend be shown? (not default)

Value

This returns the ggplot object with the dose-toxicity and dose-efficacy model fits

Functions

plotDualResponses( DLEmodel = ModelTox, DLEsamples = Samples, Effmodel = ModelEff, Effsamples = Samples ): function todo
plotDualResponses( DLEmodel = ModelTox, DLEsamples = missing, Effmodel = ModelEff, Effsamples = missing ): Plot the DLE and efficacy curve side by side given a DLE model and an efficacy model without any samples

Examples

## we need a data object with doses >= 1:
data <-DataDual(x=c(25,50,25,50,75,300,250,150),
                y=c(0,0,0,0,0,1,1,0),
                w=c(0.31,0.42,0.59,0.45,0.6,0.7,0.6,0.52),
                doseGrid=seq(25,300,25),
                placebo=FALSE)
##plot the dose-DLE and dose-efficacy curves in two plots with DLE and efficacy samples
##define the DLE model which must be of 'ModelTox' class 
##(e.g 'LogisticIndepBeta' class model)
DLEmodel<-LogisticIndepBeta(binDLE=c(1.05,1.8),DLEweights=c(3,3),DLEdose=c(25,300),data=data)
## define the efficacy model which must be of 'ModelEff' class
## (e.g 'Effloglog' class)
Effmodel<-Effloglog(Eff=c(1.223,2.513),Effdose=c(25,300),nu=c(a=1,b=0.025),data=data,c=0)
##define the DLE sample of 'Samples' class
##set up the same data set in class 'Data' for MCMC sampling for DLE
data1 <- Data(x=data@x,y=data@y,doseGrid=data@doseGrid)
##Specify the options for MCMC
options <- McmcOptions(burnin=100,step=2,samples=1000)


DLEsamples <- mcmc(data=data1,model=DLEmodel,options=options)
##define the efficacy sample of 'Samples' class
Effsamples <- mcmc(data=data,model=Effmodel,options=options)
##plot the dose-DLE and dose-efficacy curves with two plot side by side.
##For each curve the 95% credibility interval of the two samples are alos given
plotDualResponses(DLEmodel=DLEmodel,DLEsamples=DLEsamples,
         Effmodel=Effmodel,Effsamples=Effsamples,
         data=data)
## we need a data object with doses >= 1:
data <-DataDual(x=c(25,50,25,50,75,300,250,150),
                y=c(0,0,0,0,0,1,1,0),
                w=c(0.31,0.42,0.59,0.45,0.6,0.7,0.6,0.52),
                doseGrid=seq(25,300,25),
                placebo=FALSE)
##plot the dose-DLE and dose-efficacy curves in two plots without DLE and efficacy samples
##define the DLE model which must be of 'ModelTox' class 
##(e.g 'LogisticIndepBeta' class model)
DLEmodel<-LogisticIndepBeta(binDLE=c(1.05,1.8),DLEweights=c(3,3),DLEdose=c(25,300),data=data)
## define the efficacy model which must be of 'ModelEff' class
## (e.g 'Effloglog' class)
Effmodel<-Effloglog(Eff=c(1.223,2.513),Effdose=c(25,300),nu=c(a=1,b=0.025),data=data,c=0)
##plot the dose-DLE and dose-efficacy curves with two plot side by side.
plotDualResponses(DLEmodel=DLEmodel,
                  Effmodel=Effmodel,
                  data=data)
## we need a data object with doses >= 1:
data <-DataDual(x=c(25,50,25,50,75,300,250,150),
                y=c(0,0,0,0,0,1,1,0),
                w=c(0.31,0.42,0.59,0.45,0.6,0.7,0.6,0.52),
                doseGrid=seq(25,300,25),
                placebo=FALSE)
##plot the dose-DLE and dose-efficacy curves in two plots with DLE and efficacy samples
##define the DLE model which must be of 'ModelTox' class 
##(e.g 'LogisticIndepBeta' class model)
DLEmodel<-LogisticIndepBeta(binDLE=c(1.05,1.8),DLEweights=c(3,3),DLEdose=c(25,300),data=data)
## define the efficacy model which must be of 'ModelEff' class
## (e.g 'Effloglog' class)
Effmodel<-Effloglog(Eff=c(1.223,2.513),Effdose=c(25,300),nu=c(a=1,b=0.025),data=data,c=0)
##define the DLE sample of 'Samples' class
##set up the same data set in class 'Data' for MCMC sampling for DLE
data1 <- Data(x=data@x,y=data@y,doseGrid=data@doseGrid)
##Specify the options for MCMC
options <- McmcOptions(burnin=100,step=2,samples=1000)


DLEsamples <- mcmc(data=data1,model=DLEmodel,options=options)
##define the efficacy sample of 'Samples' class
Effsamples <- mcmc(data=data,model=Effmodel,options=options)
##plot the dose-DLE and dose-efficacy curves with two plot side by side.
##For each curve the 95% credibility interval of the two samples are alos given
plotDualResponses(DLEmodel=DLEmodel,DLEsamples=DLEsamples,
         Effmodel=Effmodel,Effsamples=Effsamples,
         data=data)
## we need a data object with doses >= 1:
data <-DataDual(x=c(25,50,25,50,75,300,250,150),
                y=c(0,0,0,0,0,1,1,0),
                w=c(0.31,0.42,0.59,0.45,0.6,0.7,0.6,0.52),
                doseGrid=seq(25,300,25),
                placebo=FALSE)
##plot the dose-DLE and dose-efficacy curves in two plots without DLE and efficacy samples
##define the DLE model which must be of 'ModelTox' class 
##(e.g 'LogisticIndepBeta' class model)
DLEmodel<-LogisticIndepBeta(binDLE=c(1.05,1.8),DLEweights=c(3,3),DLEdose=c(25,300),data=data)
## define the efficacy model which must be of 'ModelEff' class
## (e.g 'Effloglog' class)
Effmodel<-Effloglog(Eff=c(1.223,2.513),Effdose=c(25,300),nu=c(a=1,b=0.025),data=data,c=0)
##plot the dose-DLE and dose-efficacy curves with two plot side by side.
plotDualResponses(DLEmodel=DLEmodel,
                  Effmodel=Effmodel,
                  data=data)

Plot the gain curve in addition with the dose-DLE and dose-efficacy curve using a given DLE pseudo model, a DLE sample, a given efficacy pseudo model and an efficacy sample

Description

Plot the gain curve in addition with the dose-DLE and dose-efficacy curve using a given DLE pseudo model, a DLE sample, a given efficacy pseudo model and an efficacy sample

Plot the gain curve in addition with the dose-DLE and dose-efficacy curve using a given DLE pseudo model, and a given efficacy pseudo model

Usage

plotGain(DLEmodel, DLEsamples, Effmodel, Effsamples, data, ...)

## S4 method for signature 'ModelTox,Samples,ModelEff,Samples'
plotGain(DLEmodel, DLEsamples, Effmodel, Effsamples, data, ...)

## S4 method for signature 'ModelTox,missing,ModelEff,missing'
plotGain(DLEmodel, DLEsamples, Effmodel, Effsamples, data, ...)
plotGain(DLEmodel, DLEsamples, Effmodel, Effsamples, data, ...)

## S4 method for signature 'ModelTox,Samples,ModelEff,Samples'
plotGain(DLEmodel, DLEsamples, Effmodel, Effsamples, data, ...)

## S4 method for signature 'ModelTox,missing,ModelEff,missing'
plotGain(DLEmodel, DLEsamples, Effmodel, Effsamples, data, ...)

Arguments

`DLEmodel`	the dose-DLE model of `ModelTox` class object
`DLEsamples`	the DLE sample of `Samples` class object
`Effmodel`	the dose-efficacy model of `ModelEff` class object
`Effsamples`	the efficacy sample of of `Samples` class object
`data`	the data input of `DataDual` class object
`...`	not used

Value

This returns the ggplot object for the plot

Functions

plotGain( DLEmodel = ModelTox, DLEsamples = Samples, Effmodel = ModelEff, Effsamples = Samples ): Standard method
plotGain( DLEmodel = ModelTox, DLEsamples = missing, Effmodel = ModelEff, Effsamples = missing ): Standard method

Examples

## we need a data object with doses >= 1:
data <-DataDual(x=c(25,50,25,50,75,300,250,150),
                y=c(0,0,0,0,0,1,1,0),
                w=c(0.31,0.42,0.59,0.45,0.6,0.7,0.6,0.52),
                doseGrid=seq(25,300,25),
                placebo=FALSE)
##plot the dose-DLE , dose-efficacy and gain curve in the same plot with DLE and efficacy samples
##define the DLE model which must be of 'ModelTox' class 
##(e.g 'LogisticIndepBeta' class model)
DLEmodel<-LogisticIndepBeta(binDLE=c(1.05,1.8),DLEweights=c(3,3),DLEdose=c(25,300),data=data)
## define the efficacy model which must be of 'ModelEff' class
## (e.g 'Effloglog' class)
Effmodel<-Effloglog(Eff=c(1.223,2.513),Effdose=c(25,300),nu=c(a=1,b=0.025),data=data,c=0)
##define the DLE sample of 'Samples' class
##set up the same data set in class 'Data' for MCMC sampling for DLE
data1 <- Data(x=data@x,y=data@y,doseGrid=data@doseGrid)

##Define the options for MCMC
options <- McmcOptions(burnin=100,step=2,samples=1000)


DLEsamples <- mcmc(data=data1,model=DLEmodel,options=options)
##define the efficacy sample of 'Samples' class
Effsamples <- mcmc(data=data,model=Effmodel,options=options)
##plot the three curves of mean values of the DLEsamples, Effsamples and 
##gain value samples (obtained within this plotGain function) at all dose levels
plotGain(DLEmodel=DLEmodel,DLEsamples=DLEsamples,
         Effmodel=Effmodel,Effsamples=Effsamples,
         data=data)
## we need a data object with doses >= 1:
data <-DataDual(x=c(25,50,25,50,75,300,250,150),
                y=c(0,0,0,0,0,1,1,0),
                w=c(0.31,0.42,0.59,0.45,0.6,0.7,0.6,0.52),
                doseGrid=seq(25,300,25),
                placebo=FALSE)
##plot the dose-DLE , dose-efficacy and gain curve in the same plot with DLE and efficacy samples
##define the DLE model which must be of 'ModelTox' class 
##(e.g 'LogisticIndepBeta' class model)
DLEmodel<-LogisticIndepBeta(binDLE=c(1.05,1.8),DLEweights=c(3,3),DLEdose=c(25,300),data=data)
## define the efficacy model which must be of 'ModelEff' class
## (e.g 'Effloglog' class)
Effmodel<-Effloglog(Eff=c(1.223,2.513),Effdose=c(25,300),nu=c(a=1,b=0.025),data=data,c=0)
##plot the three curves of using modal estimates of model parameters at all dose levels
plotGain(DLEmodel=DLEmodel,
         Effmodel=Effmodel,
         data=data)
## we need a data object with doses >= 1:
data <-DataDual(x=c(25,50,25,50,75,300,250,150),
                y=c(0,0,0,0,0,1,1,0),
                w=c(0.31,0.42,0.59,0.45,0.6,0.7,0.6,0.52),
                doseGrid=seq(25,300,25),
                placebo=FALSE)
##plot the dose-DLE , dose-efficacy and gain curve in the same plot with DLE and efficacy samples
##define the DLE model which must be of 'ModelTox' class 
##(e.g 'LogisticIndepBeta' class model)
DLEmodel<-LogisticIndepBeta(binDLE=c(1.05,1.8),DLEweights=c(3,3),DLEdose=c(25,300),data=data)
## define the efficacy model which must be of 'ModelEff' class
## (e.g 'Effloglog' class)
Effmodel<-Effloglog(Eff=c(1.223,2.513),Effdose=c(25,300),nu=c(a=1,b=0.025),data=data,c=0)
##define the DLE sample of 'Samples' class
##set up the same data set in class 'Data' for MCMC sampling for DLE
data1 <- Data(x=data@x,y=data@y,doseGrid=data@doseGrid)

##Define the options for MCMC
options <- McmcOptions(burnin=100,step=2,samples=1000)


DLEsamples <- mcmc(data=data1,model=DLEmodel,options=options)
##define the efficacy sample of 'Samples' class
Effsamples <- mcmc(data=data,model=Effmodel,options=options)
##plot the three curves of mean values of the DLEsamples, Effsamples and 
##gain value samples (obtained within this plotGain function) at all dose levels
plotGain(DLEmodel=DLEmodel,DLEsamples=DLEsamples,
         Effmodel=Effmodel,Effsamples=Effsamples,
         data=data)
## we need a data object with doses >= 1:
data <-DataDual(x=c(25,50,25,50,75,300,250,150),
                y=c(0,0,0,0,0,1,1,0),
                w=c(0.31,0.42,0.59,0.45,0.6,0.7,0.6,0.52),
                doseGrid=seq(25,300,25),
                placebo=FALSE)
##plot the dose-DLE , dose-efficacy and gain curve in the same plot with DLE and efficacy samples
##define the DLE model which must be of 'ModelTox' class 
##(e.g 'LogisticIndepBeta' class model)
DLEmodel<-LogisticIndepBeta(binDLE=c(1.05,1.8),DLEweights=c(3,3),DLEdose=c(25,300),data=data)
## define the efficacy model which must be of 'ModelEff' class
## (e.g 'Effloglog' class)
Effmodel<-Effloglog(Eff=c(1.223,2.513),Effdose=c(25,300),nu=c(a=1,b=0.025),data=data,c=0)
##plot the three curves of using modal estimates of model parameters at all dose levels
plotGain(DLEmodel=DLEmodel,
         Effmodel=Effmodel,
         data=data)

Compute the probability for a given dose, given model and samples

Description

Compute the probability for a given dose, given model and samples

Usage

prob(dose, model, samples, ...)

## S4 method for signature 'numeric,Model,Samples'
prob(dose, model, samples, ...)

## S4 method for signature 'numeric,ModelTox,Samples'
prob(dose, model, samples, ...)

## S4 method for signature 'numeric,ModelTox,missing'
prob(dose, model, samples, ...)
prob(dose, model, samples, ...)

## S4 method for signature 'numeric,Model,Samples'
prob(dose, model, samples, ...)

## S4 method for signature 'numeric,ModelTox,Samples'
prob(dose, model, samples, ...)

## S4 method for signature 'numeric,ModelTox,missing'
prob(dose, model, samples, ...)

Arguments

`dose`	the dose
`model`	the `Model` object
`samples`	the `Samples`
`...`	unused

Value

the vector (for Model objects) of probability samples.

Functions

prob(dose = numeric, model = ModelTox, samples = Samples): Compute the probability for a given dose, given Pseudo DLE model and samples
prob(dose = numeric, model = ModelTox, samples = missing): Compute the probability for a given dose, given Pseudo DLE model without samples

Examples


# create some data
data <- Data(x =c (0.1, 0.5, 1.5, 3, 6, 10, 10, 10),
             y = c(0, 0, 0, 0, 0, 0, 1, 0),
             cohort = c(0, 1, 2, 3, 4, 5, 5, 5),
             doseGrid = c(0.1, 0.5, 1.5, 3, 6,
                          seq(from=10, to=80, by=2)))

# Initialize a  model
model <- LogisticLogNormal(mean=c(-0.85, 1),
                           cov=matrix(c(1, -0.5, -0.5, 1),
                                      nrow=2),
                           refDose=56)

# Get samples from posterior
options <- McmcOptions(burnin=100,
                       step=2,
                       samples=2000)
set.seed(94)
samples <- mcmc(data, model, options)

# posterior for Prob(DLT | dose=50)
tox.prob <- prob(dose=50, model=model, samples=samples)




# create data from the 'DataDual' class
data <- DataDual(x = c(25,50,25,50,75,300,250,150),
                 y = c(0,0,0,0,0,1,1,0),
                 w = c(0.31,0.42,0.59,0.45,0.6,0.7,0.6,0.52),
                 doseGrid = seq(25,300,25))

## Initialize a model from 'ModelTox' class e.g using 'LogisticIndepBeta' model
DLEmodel <- LogisticIndepBeta(binDLE=c(1.05,1.8),
                              DLEweights=c(3,3),
                              DLEdose=c(25,300),
                              data=data)

options <- McmcOptions(burnin=100, step=2, samples=200)
DLEsamples <- mcmc(data=data,model=DLEmodel,options=options)

tox.prob <- prob(dose=100, model = DLEmodel, samples = DLEsamples)



# create data from the 'DataDual' class
data <- DataDual(x = c(25,50,25,50,75,300,250,150),
                 y = c(0,0,0,0,0,1,1,0),
                 w = c(0.31,0.42,0.59,0.45,0.6,0.7,0.6,0.52),
                 doseGrid = seq(25,300,25))

## Initialize a model from 'ModelTox' class e.g using 'LogisticIndepBeta' model
DLEmodel <- LogisticIndepBeta(binDLE=c(1.05,1.8),
                              DLEweights=c(3,3),
                              DLEdose=c(25,300),
                              data=data)

tox.prob <- prob(dose=100, model = DLEmodel)

# create some data
data <- Data(x =c (0.1, 0.5, 1.5, 3, 6, 10, 10, 10),
             y = c(0, 0, 0, 0, 0, 0, 1, 0),
             cohort = c(0, 1, 2, 3, 4, 5, 5, 5),
             doseGrid = c(0.1, 0.5, 1.5, 3, 6,
                          seq(from=10, to=80, by=2)))

# Initialize a  model
model <- LogisticLogNormal(mean=c(-0.85, 1),
                           cov=matrix(c(1, -0.5, -0.5, 1),
                                      nrow=2),
                           refDose=56)

# Get samples from posterior
options <- McmcOptions(burnin=100,
                       step=2,
                       samples=2000)
set.seed(94)
samples <- mcmc(data, model, options)

# posterior for Prob(DLT | dose=50)
tox.prob <- prob(dose=50, model=model, samples=samples)




# create data from the 'DataDual' class
data <- DataDual(x = c(25,50,25,50,75,300,250,150),
                 y = c(0,0,0,0,0,1,1,0),
                 w = c(0.31,0.42,0.59,0.45,0.6,0.7,0.6,0.52),
                 doseGrid = seq(25,300,25))

## Initialize a model from 'ModelTox' class e.g using 'LogisticIndepBeta' model
DLEmodel <- LogisticIndepBeta(binDLE=c(1.05,1.8),
                              DLEweights=c(3,3),
                              DLEdose=c(25,300),
                              data=data)

options <- McmcOptions(burnin=100, step=2, samples=200)
DLEsamples <- mcmc(data=data,model=DLEmodel,options=options)

tox.prob <- prob(dose=100, model = DLEmodel, samples = DLEsamples)



# create data from the 'DataDual' class
data <- DataDual(x = c(25,50,25,50,75,300,250,150),
                 y = c(0,0,0,0,0,1,1,0),
                 w = c(0.31,0.42,0.59,0.45,0.6,0.7,0.6,0.52),
                 doseGrid = seq(25,300,25))

## Initialize a model from 'ModelTox' class e.g using 'LogisticIndepBeta' model
DLEmodel <- LogisticIndepBeta(binDLE=c(1.05,1.8),
                              DLEweights=c(3,3),
                              DLEdose=c(25,300),
                              data=data)

tox.prob <- prob(dose=100, model = DLEmodel)

Shorthand for probit function

Description

Shorthand for probit function

Usage

probit(x)
probit(x)

Arguments

`x`	the function argument

Value

the probit(x)

Initialization function for the "ProbitLogNormal" class

Description

Initialization function for the "ProbitLogNormal" class

Usage

ProbitLogNormal(mu, Sigma, refDose = 1, useLogDose = FALSE)
ProbitLogNormal(mu, Sigma, refDose = 1, useLogDose = FALSE)

Arguments

`mu`	the prior mean vector
`Sigma`	the prior covariance matrix
`refDose`	the reference dose $x^{*}$ , default 1 (no standardization)
`useLogDose`	should the log of (standardized) dose be used? (not default)

Value

the ProbitLogNormal object

Probit model with bivariate log normal prior

Description

This is probit regression model with a bivariate normal prior on the intercept and log slope. The covariate is the dose $x$ itself, potentially divided by a reference dose $x^{*}$ , or the logarithm of it:

Details

$probit[p(x)] = \alpha + \beta \cdot x/x^{*}$

$probit[p(x)] = \alpha + \beta \cdot \log(x/x^{*})$

in case that the option useLogDose is TRUE. Here $p(x)$ is the probability of observing a DLT for a given dose $x$ .

The prior is

$(\alpha, \log(\beta)) \sim Normal(\mu, \Sigma)$

The slots of this class contain the mean vector and the covariance matrix of the bivariate normal distribution, as well as the reference dose. Note that the parametrization inside the class uses alpha0 and alpha1.

This model is also used in the DualEndpoint classes, so this class can be used to check the prior assumptions on the dose-toxicity model - even when sampling from the prior distribution of the dual endpoint model is not possible.

Slots

mu: the prior mean vector $\mu$
Sigma: the prior covariance matrix $\Sigma$
refDose: the reference dose $x^{*}$
useLogDose: should the log of (standardized) dose be used?

Examples

model <- ProbitLogNormal(mu = c(-0.85, 1),
                           Sigma = matrix(c(1, -0.5, -0.5, 1), nrow = 2))

## we can also specify a reference dose, and use a log transformation of
## standardized dose in the model:
model <- ProbitLogNormal(mu = c(-0.85, 1),
                         Sigma = matrix(c(1, -0.5, -0.5, 1), nrow = 2),
                         refDose = 7.2,
                         useLogDose=TRUE)


model <- ProbitLogNormal(mu = c(-0.85, 1),
                           Sigma = matrix(c(1, -0.5, -0.5, 1), nrow = 2))

## we can also specify a reference dose, and use a log transformation of
## standardized dose in the model:
model <- ProbitLogNormal(mu = c(-0.85, 1),
                         Sigma = matrix(c(1, -0.5, -0.5, 1), nrow = 2),
                         refDose = 7.2,
                         useLogDose=TRUE)

Initialization function for 'PseudoDualFlexiSimulations' class

Description

Initialization function for 'PseudoDualFlexiSimulations' class

Usage

PseudoDualFlexiSimulations(sigma2betaWest, ...)
PseudoDualFlexiSimulations(sigma2betaWest, ...)

Arguments

`sigma2betaWest`	please refer to `PseudoDualFlexiSimulations` class object
`...`	additional parameters from `PseudoDualSimulations`

Value

the PseudoDualFlexiSimulations object

This is a class which captures the trial simulations design using both the DLE and efficacy responses. The design of model from `ModelTox` class and the efficacy model from `EffFlexi` class It contains all slots from `GeneralSimulations`, `PseudoSimulations` and `PseudoDualSimulations` object. In comparison to the parent class `PseudoDualSimulations`, it contains additional slots to capture the sigma2betaW estimates.

Description

This is a class which captures the trial simulations design using both the DLE and efficacy responses. The design of model from ModelTox class and the efficacy model from EffFlexi class It contains all slots from GeneralSimulations, PseudoSimulations and PseudoDualSimulations object. In comparison to the parent class PseudoDualSimulations, it contains additional slots to capture the sigma2betaW estimates.

Slots

sigma2betaWest: the vector of the final posterior mean sigma2betaW estimates

Initialization function for 'DualPseudoSimulations' class

Description

Initialization function for 'DualPseudoSimulations' class

Usage

PseudoDualSimulations(
  fitEff,
  FinalGstarEstimates,
  FinalGstarAtDoseGrid,
  FinalGstarCIs,
  FinalGstarRatios,
  FinalOptimalDose,
  FinalOptimalDoseAtDoseGrid,
  sigma2est,
  ...
)
PseudoDualSimulations(
  fitEff,
  FinalGstarEstimates,
  FinalGstarAtDoseGrid,
  FinalGstarCIs,
  FinalGstarRatios,
  FinalOptimalDose,
  FinalOptimalDoseAtDoseGrid,
  sigma2est,
  ...
)

Arguments

`fitEff`	please refer to `PseudoDualSimulations` class object
`FinalGstarEstimates`	please refer to `PseudoDualSimulations` class object
`FinalGstarAtDoseGrid`	please refer to `PseudoDualSimulations` class object
`FinalGstarCIs`	please refer to `PseudoDualSimulations` class object
`FinalGstarRatios`	please refer to `PseudoDualSimulations` class object
`FinalOptimalDose`	please refer to `PseudoDualSimulations` class object
`FinalOptimalDoseAtDoseGrid`	please refer to `PseudoDualSimulations` class object
`sigma2est`	please refer to `PseudoDualSimulations` class object
`...`	additional parameters from `PseudoSimulations`

Value

the PseudoDualSimulations object

This is a class which captures the trial simulations design using both the DLE and efficacy responses. The design of model from `ModelTox` class and the efficacy model from `ModelEff` class (except `EffFlexi` class). It contains all slots from `GeneralSimulations` and `PseudoSimulations` object. In comparison to the parent class `PseudoSimulations`, it contains additional slots to capture the dose-efficacy curve and the sigma2 estimates.

Description

This is a class which captures the trial simulations design using both the DLE and efficacy responses. The design of model from ModelTox class and the efficacy model from ModelEff class (except EffFlexi class). It contains all slots from GeneralSimulations and PseudoSimulations object. In comparison to the parent class PseudoSimulations, it contains additional slots to capture the dose-efficacy curve and the sigma2 estimates.

Slots

fitEff: list of the final values. If DLE and efficacy samples are gerneated, it contains the final fitted values. If no DLE and efficacy samples are used, it contains the modal estimates of the parameters in the two models and the posterior estimates of the probabilities of the occurrence of a DLE and the expected efficacy responses.
FinalGstarEstimates: a vector of the final estimates of Gstar at the end of each simulations.
FinalGstarAtDoseGrid: is a vectorof the final estimates of Gstar at dose Grid at the end of each simulations
FinalGstarCIs: is the list of all 95% credibility interval of the final estimates of Gstar
FinalGstarRatios: is the vector of the ratios of the CI, the ratio of the upper to the lower 95% credibility interval of the final estimates of Gstar
FinalOptimalDose: is the vector of the final optimal dose, the minimum of the final TDtargetEndOfTrial estimates and Gstar estimates
FinalOptimalDoseAtDoseGrid: is the vector of the final optimal dose, the minimum of the final TDtargetEndOfTrial estimates and Gstar estimates at dose Grid
sigma2est: the vector of the final posterior mean sigma2 estimates

Class for the summary of the dual responses simulations using pseudo models

Description

It contains all slots from PseudoSimulationsSummary object. In addition to the slots in the parent class PseudoSimulationsSummary, it contains four more slots for the efficacy model fit information.

Details

Note that objects should not be created by users, therefore no initialization function is provided for this class.

Slots

targetGstar: the target dose level such that its gain value is at maximum
targetGstarAtDoseGrid: the dose level at dose Grid closest and below Gstar
GstarSummary: the six-number table summary (lowest, 25th, 50th (median), 75th percentile, mean and highest value) of the final Gstar values obtained across all simulations
ratioGstarSummary: the six-number summary table of the ratios of the upper to the lower 95% credibility intervals of the final Gstar across all simulations
EffFitAtDoseMostSelected: fitted expected mean efficacy value at dose most often selected
meanEffFit: list with mean, lower (2.5 efficacy value at each dose level.

Initialization function of the 'PseudoSimulations' class

Description

Initialization function of the 'PseudoSimulations' class

Usage

PseudoSimulations(
  fit,
  FinalTDtargetDuringTrialEstimates,
  FinalTDtargetEndOfTrialEstimates,
  FinalTDtargetDuringTrialAtDoseGrid,
  FinalTDtargetEndOfTrialAtDoseGrid,
  FinalTDEOTCIs,
  FinalTDEOTRatios,
  FinalCIs,
  FinalRatios,
  stopReasons,
  ...
)
PseudoSimulations(
  fit,
  FinalTDtargetDuringTrialEstimates,
  FinalTDtargetEndOfTrialEstimates,
  FinalTDtargetDuringTrialAtDoseGrid,
  FinalTDtargetEndOfTrialAtDoseGrid,
  FinalTDEOTCIs,
  FinalTDEOTRatios,
  FinalCIs,
  FinalRatios,
  stopReasons,
  ...
)

Arguments

`fit`	please refer to `PseudoSimulations` class object
`FinalTDtargetDuringTrialEstimates`	please refer to `PseudoSimulations` class object
`FinalTDtargetEndOfTrialEstimates`	please refer to `PseudoSimulations` class object
`FinalTDtargetDuringTrialAtDoseGrid`	please refer to `PseudoSimulations` class object
`FinalTDtargetEndOfTrialAtDoseGrid`	please refer to `PseudoSimulations` class object
`FinalTDEOTCIs`	please refer to `PseudoSimulations` class object
`FinalTDEOTRatios`	please refer to `PseudoSimulations` class object
`FinalCIs`	please refer to `PseudoSimulations` class object
`FinalRatios`	please refer to `PseudoSimulations` class object
`stopReasons`	please refer to `PseudoSimulations` class object
`...`	additional parameters from `GeneralSimulations`

Value

the PseudoSimulations object

This is a class which captures the trial simulations from designs using pseudo model. The design for DLE only responses and model from `ModelTox` class object. It contains all slots from `GeneralSimulations` object. Additional slots fit and stopReasons compared to the general class `GeneralSimulations`.

Description

This is a class which captures the trial simulations from designs using pseudo model. The design for DLE only responses and model from ModelTox class object. It contains all slots from GeneralSimulations object. Additional slots fit and stopReasons compared to the general class GeneralSimulations.

Slots

fit: list of the final values. If samples are involved, these are the final fitted values. If no samples are involved, these are included the final modal estimates of the model parameters and the posterior estimates of the probabilities of the occurrence of a DLE.
FinalTDtargetDuringTrialEstimates: the vector of all final estimates (the last estimate of) the TDtargetDuringTrial at the end of each simultaions/when each trial stops
FinalTDtargetEndOfTrialEstimates: vector of all final estimates or the last estimate of the TDtargetEndOfTrial when each trial stops
FinalTDtargetDuringTrialAtDoseGrid: vector of the dose levels at dose grid closest below the final TDtargetDuringTrial estimates
FinalTDtargetEndOfTrialAtDoseGrid: vector of the dose levels at dose grid closest below the final TDtargetEndOfTrial estimates
FinalTDEOTCIs: is the list of all 95% credibility interval of the final estimates of the TDtargetEndOfTrial
FinalTDEOTRatios: is the vector of the ratios of the CI, the raatio of the upper to the lower 95% credibility intervals of the final estimates of the TDtargetEndOfTrial
FinalCIs: list of all the final 95% credibility intervals of the TDtargetEndofTrial estimates or of the final optimal dose estimates when DLE and efficacy responses are incorporated after each simulations
FinalRatios: vector of all the final ratios, the ratios of the upper to the lower 95% credibility interval of the final estimates of the TDtargetEndOfTrial or of the final optimal dose estiamtes (when DLE and efficacy responses are incorporated) after each simulations
stopReasons: todo: add slot description

Class for the summary of pseudo-models simulations output

Description

Note that objects should not be created by users, therefore no initialization function is provided for this class.

Slots

targetEndOfTrial: the target probability of DLE wanted at the end of a trial
targetDoseEndOfTrial: the dose level corresponds to the target probability of DLE wanted at the end of a trial, TDEOT
targetDoseEndOfTrialAtDoseGrid: the dose level at dose grid corresponds to the target probability of DLE wanted at the end of a trial
targetDuringTrial: the target probability of DLE wanted during a trial
targetDoseDuringTrial: the dose level corresponds to the target probability of DLE wanted during the trial. TDDT
targetDoseDuringTrialAtDoseGrid: the dose level at dose grid corresponds to the target probability of DLE wanted during a trial
TDEOTSummary: the six-number table summary, include the lowest, the 25th precentile (lower quatile), the 50th percentile (median), the mean, the 27th percentile and the highest values of the final dose levels obtained corresponds to the target probability of DLE want at the end of a trial across all simulations
TDDTSummary: the six-number table summary, include the lowest, the 25th precentile (lower quatile), the 50th percentile (median), the mean, the 27th percentile and the highest values of the final dose levels obtained corresponds to the target probability of DLE want during a trial across all simulations
FinalDoseRecSummary: the six-number table summary, include the lowest, the 25th precentile (lower quatile), the 50th percentile (median), the mean, the 27th percentile and the highest values of the final optimal doses, which is either the TDEOT when only DLE response are incorporated into the escalation procedure or the minimum of the TDEOT and Gstar when DLE and efficacy responses are incorporated, across all simulations
ratioTDEOTSummary: the six-number summary table of the final ratios of the upper to the lower 95% credibility intervals of the final TDEOTs across all simulations
FinalRatioSummary: the six-number summary table of the final ratios of the upper to the lower 95% credibility intervals of the final optimal doses across all simulations #@slot doseRec the dose level that will be recommend for subsequent study
nsim: number of simulations
propDLE: proportions of DLE in the trials
meanToxRisk: mean toxicity risks for the patients
doseSelected: doses selected as MTD (targetDoseEndOfTrial)
toxAtDosesSelected: true toxicity at doses selected
propAtTargetEndOfTrial: Proportion of trials selecting at the doseGrid closest below the MTD, the targetDoseEndOfTrial
propAtTargetDuringTrial: Proportion of trials selecting at the doseGrid closest below the targetDoseDuringTrial
doseMostSelected: dose most often selected as MTD
obsToxRateAtDoseMostSelected: observed toxicity rate at dose most often selected
nObs: number of patients overall
nAboveTargetEndOfTrial: number of patients treated above targetDoseEndOfTrial
nAboveTargetDuringTrial: number of patients treated above targetDoseDuringTrial
doseGrid: the dose grid that has been used
fitAtDoseMostSelected: fitted toxicity rate at dose most often selected
meanFit: list with the average, lower (2.5 quantiles of the mean fitted toxicity at each dose level

Convert prior quantiles (lower, median, upper) to logistic (log) normal model

Description

This function uses generalised simulated annealing to optimise a LogisticNormal model to be as close as possible to the given prior quantiles.

Usage

Quantiles2LogisticNormal(
  dosegrid,
  refDose,
  lower,
  median,
  upper,
  level = 0.95,
  logNormal = FALSE,
  parstart = NULL,
  parlower = c(-10, -10, 0, 0, -0.95),
  parupper = c(10, 10, 10, 10, 0.95),
  seed = 12345,
  verbose = TRUE,
  control = list(threshold.stop = 0.01, maxit = 50000, temperature = 50000, max.time =
    600)
)
Quantiles2LogisticNormal(
  dosegrid,
  refDose,
  lower,
  median,
  upper,
  level = 0.95,
  logNormal = FALSE,
  parstart = NULL,
  parlower = c(-10, -10, 0, 0, -0.95),
  parupper = c(10, 10, 10, 10, 0.95),
  seed = 12345,
  verbose = TRUE,
  control = list(threshold.stop = 0.01, maxit = 50000, temperature = 50000, max.time =
    600)
)

Arguments

`dosegrid`	the dose grid
`refDose`	the reference dose
`lower`	the lower quantiles
`median`	the medians
`upper`	the upper quantiles
`level`	the credible level of the (lower, upper) intervals (default: 0.95)
`logNormal`	use the log-normal prior? (not default) otherwise, the normal prior for the logistic regression coefficients is used
`parstart`	starting values for the parameters. By default, these are determined from the medians supplied.
`parlower`	lower bounds on the parameters (intercept alpha and the slope beta, the corresponding standard deviations and the correlation.)
`parupper`	upper bounds on the parameters
`seed`	seed for random number generation
`verbose`	be verbose? (default)
`control`	additional options for the optimisation routine, see `GenSA` for more details

Value

a list with the best approximating model (LogisticNormal or LogisticLogNormal), the resulting quantiles, the required quantiles and the distance to the required quantiles, as well as the final parameters (which could be used for running the algorithm a second time)

A Reference Class to represent sequentially updated reporting objects.

Description

A Reference Class to represent sequentially updated reporting objects.

Fields

object: The object from which to report
df: the data frame to which columns are sequentially added
dfNames: the names to which strings are sequentially added

Initialization function for "RuleDesign"

Description

Initialization function for "RuleDesign"

Usage

RuleDesign(nextBest, cohortSize, data, startingDose)
RuleDesign(nextBest, cohortSize, data, startingDose)

Arguments

`nextBest`	see `RuleDesign`
`cohortSize`	see `RuleDesign`
`data`	see `RuleDesign`
`startingDose`	see `RuleDesign`

Value

the RuleDesign object

Class for rule-based designs

Description

The difference to Design class is that model, stopping and increments slots are missing.

Slots

nextBest: how to find the next best dose, an object of class NextBest
cohortSize: rules for the cohort sizes, an object of class CohortSize
data: what is the dose grid, any previous data, etc., contained in an object of class Data
startingDose: what is the starting dose? Must lie on the grid in data

Examples


emptydata <- Data(doseGrid = c(5, 10, 15, 25, 35, 50, 80))

# inizialing a 3+3 design with constant cohort size of 3 and
# starting dose equal 5
myDesign <- RuleDesign(nextBest = NextBestThreePlusThree(),
                       cohortSize = CohortSizeConst(size=3L),
                       data = emptydata,
                       startingDose = 5)
  
  
emptydata <- Data(doseGrid = c(5, 10, 15, 25, 35, 50, 80))

# inizialing a 3+3 design with constant cohort size of 3 and
# starting dose equal 5
myDesign <- RuleDesign(nextBest = NextBestThreePlusThree(),
                       cohortSize = CohortSizeConst(size=3L),
                       data = emptydata,
                       startingDose = 5)

Initialization function for "Samples"

Description

Initialization function for "Samples"

Usage

Samples(data, options)
Samples(data, options)

Arguments

`data`	see `Samples`
`options`	see `Samples`

Value

the Samples object

Class for the MCMC output

Description

Class for the MCMC output

Slots

data: a list where each entry contains the samples of a (vector-valued) parameter in a vector/matrix in the format (number of samples) x (dimension of the parameter).
options: the McmcOptions which have been used

Examples


# Saving the mcmc options which were used to generate the samples
options <- McmcOptions(burnin=10000,
                       step=2,
                       samples=10000)

# Create an object of class 'Samples'
# Here the parameters 'alpha' and 'beta' are randomly generated. Of coure in 
# a real example these would come as an output from mcmc procedures
samples <- Samples(data=list(alpha=rnorm(10000),beta=rnorm(10000)),
                   options=options)

# Saving the mcmc options which were used to generate the samples
options <- McmcOptions(burnin=10000,
                       step=2,
                       samples=10000)

# Create an object of class 'Samples'
# Here the parameters 'alpha' and 'beta' are randomly generated. Of coure in 
# a real example these would come as an output from mcmc procedures
samples <- Samples(data=list(alpha=rnorm(10000),beta=rnorm(10000)),
                   options=options)

Compute the number of samples for a given MCMC options triple

Description

Compute the number of samples for a given MCMC options triple

Usage

sampleSize(mcmcOptions)
sampleSize(mcmcOptions)

Arguments

mcmcOptions

the McmcOptions object

Value

the resulting sample size

Examples


# Set up MCMC option in order to have a burn-in of 10000 iterations and
# then take every other iteration up to a collection of 10000 samples
options <- McmcOptions(burnin=10000,
                       step=2,
                       samples=10000)

sampleSize(options)

# Set up MCMC option in order to have a burn-in of 10000 iterations and
# then take every other iteration up to a collection of 10000 samples
options <- McmcOptions(burnin=10000,
                       step=2,
                       samples=10000)

sampleSize(options)

Helper function to set and save the RNG seed

Description

This is basically copied from simulate.lm

Usage

setSeed(seed = NULL)
setSeed(seed = NULL)

Arguments

seed

an object specifying if and how the random number generator should be initialized (“seeded”). Either NULL (default) or an integer that will be used in a call to set.seed before simulating the response vectors. If set, the value is saved as the seed slot of the returned object. The default, NULL will not change the random generator state.

Value

The RNGstate will be returned, in order to call this function with this input to reproduce the obtained simulation results

Author(s)

Daniel Sabanes Bove [email protected]

Show the summary of the dual-endpoint simulations

Description

Show the summary of the dual-endpoint simulations

Usage

## S4 method for signature 'DualSimulationsSummary'
show(object)
## S4 method for signature 'DualSimulationsSummary'
show(object)

Arguments

object

the DualSimulationsSummary object we want to print

Value

invisibly returns a data frame of the results with one row and appropriate column names

Examples


# Define the dose-grid
emptydata <- DataDual(doseGrid = c(1, 3, 5, 10, 15, 20, 25, 30))

# Initialize the CRM model 
model <- DualEndpointRW(mu = c(0, 1),
                        Sigma = matrix(c(1, 0, 0, 1), nrow=2),
                        sigma2betaW = 0.01,
                        sigma2W = c(a=0.1, b=0.1),
                        rho = c(a=1, b=1),
                        smooth="RW1")

# Choose the rule for selecting the next dose 
myNextBest <- NextBestDualEndpoint(target=c(0.9, 1),
                                   overdose=c(0.35, 1),
                                   maxOverdoseProb=0.25)

# Choose the rule for the cohort-size 
mySize1 <- CohortSizeRange(intervals=c(0, 30),
                           cohortSize=c(1, 3))
mySize2 <- CohortSizeDLT(DLTintervals=c(0, 1),
                         cohortSize=c(1, 3))
mySize <- maxSize(mySize1, mySize2)

# Choose the rule for stopping
myStopping4 <- StoppingTargetBiomarker(target=c(0.9, 1),
                                       prob=0.5)
# small number of patients just for illustration here
myStopping <- myStopping4 | StoppingMinPatients(10)

# Choose the rule for dose increments
myIncrements <- IncrementsRelative(intervals=c(0, 20),
                                   increments=c(1, 0.33))

# Initialize the design
design <- DualDesign(model = model,
                     data = emptydata,
                     nextBest = myNextBest,
                     stopping = myStopping,
                     increments = myIncrements,
                     cohortSize = CohortSizeConst(3),
                     startingDose = 3)
  
# define scenarios for the TRUE toxicity and efficacy profiles
betaMod <- function (dose, e0, eMax, delta1, delta2, scal)
{
  maxDens <- (delta1^delta1) * (delta2^delta2)/((delta1 + delta2)^(delta1 + delta2))
  dose <- dose/scal
  e0 + eMax/maxDens * (dose^delta1) * (1 - dose)^delta2
}

trueBiomarker <- function(dose)
{
  betaMod(dose, e0=0.2, eMax=0.6, delta1=5, delta2=5 * 0.5 / 0.5, scal=100)
}

trueTox <- function(dose)
{
  pnorm((dose-60)/10)
}

# Draw the TRUE profiles
par(mfrow=c(1, 2))
curve(trueTox(x), from=0, to=80)
curve(trueBiomarker(x), from=0, to=80)

# Run the simulation on the desired design
# We only generate 1 trial outcome here for illustration, for the actual study 
# Also for illustration purpose, we will use 5 burn-ins to generate 20 samples
# this should be increased of course
mySims <- simulate(design,
                   trueTox=trueTox,
                   trueBiomarker=trueBiomarker,
                   sigma2W=0.01,
                   rho=0,
                   nsim=1,
                   parallel=FALSE,
                   seed=3,
                   startingDose=6,
                   mcmcOptions =
                     McmcOptions(burnin=5,
                                 step=1,
                                 samples=20))

# Show the summary of the Simulations
show(summary(mySims,
             trueTox = trueTox,
             trueBiomarker = trueBiomarker))



  
# Define the dose-grid
emptydata <- DataDual(doseGrid = c(1, 3, 5, 10, 15, 20, 25, 30))

# Initialize the CRM model 
model <- DualEndpointRW(mu = c(0, 1),
                        Sigma = matrix(c(1, 0, 0, 1), nrow=2),
                        sigma2betaW = 0.01,
                        sigma2W = c(a=0.1, b=0.1),
                        rho = c(a=1, b=1),
                        smooth="RW1")

# Choose the rule for selecting the next dose 
myNextBest <- NextBestDualEndpoint(target=c(0.9, 1),
                                   overdose=c(0.35, 1),
                                   maxOverdoseProb=0.25)

# Choose the rule for the cohort-size 
mySize1 <- CohortSizeRange(intervals=c(0, 30),
                           cohortSize=c(1, 3))
mySize2 <- CohortSizeDLT(DLTintervals=c(0, 1),
                         cohortSize=c(1, 3))
mySize <- maxSize(mySize1, mySize2)

# Choose the rule for stopping
myStopping4 <- StoppingTargetBiomarker(target=c(0.9, 1),
                                       prob=0.5)
# small number of patients just for illustration here
myStopping <- myStopping4 | StoppingMinPatients(10)

# Choose the rule for dose increments
myIncrements <- IncrementsRelative(intervals=c(0, 20),
                                   increments=c(1, 0.33))

# Initialize the design
design <- DualDesign(model = model,
                     data = emptydata,
                     nextBest = myNextBest,
                     stopping = myStopping,
                     increments = myIncrements,
                     cohortSize = CohortSizeConst(3),
                     startingDose = 3)
  
# define scenarios for the TRUE toxicity and efficacy profiles
betaMod <- function (dose, e0, eMax, delta1, delta2, scal)
{
  maxDens <- (delta1^delta1) * (delta2^delta2)/((delta1 + delta2)^(delta1 + delta2))
  dose <- dose/scal
  e0 + eMax/maxDens * (dose^delta1) * (1 - dose)^delta2
}

trueBiomarker <- function(dose)
{
  betaMod(dose, e0=0.2, eMax=0.6, delta1=5, delta2=5 * 0.5 / 0.5, scal=100)
}

trueTox <- function(dose)
{
  pnorm((dose-60)/10)
}

# Draw the TRUE profiles
par(mfrow=c(1, 2))
curve(trueTox(x), from=0, to=80)
curve(trueBiomarker(x), from=0, to=80)

# Run the simulation on the desired design
# We only generate 1 trial outcome here for illustration, for the actual study 
# Also for illustration purpose, we will use 5 burn-ins to generate 20 samples
# this should be increased of course
mySims <- simulate(design,
                   trueTox=trueTox,
                   trueBiomarker=trueBiomarker,
                   sigma2W=0.01,
                   rho=0,
                   nsim=1,
                   parallel=FALSE,
                   seed=3,
                   startingDose=6,
                   mcmcOptions =
                     McmcOptions(burnin=5,
                                 step=1,
                                 samples=20))

# Show the summary of the Simulations
show(summary(mySims,
             trueTox = trueTox,
             trueBiomarker = trueBiomarker))

Show the summary of the simulations

Description

Show the summary of the simulations

Usage

## S4 method for signature 'GeneralSimulationsSummary'
show(object)
## S4 method for signature 'GeneralSimulationsSummary'
show(object)

Arguments

object

the GeneralSimulationsSummary object we want to print

Value

invisibly returns a data frame of the results with one row and appropriate column names

Show the summary of Pseudo Dual simulations summary

Description

Show the summary of Pseudo Dual simulations summary

Usage

## S4 method for signature 'PseudoDualSimulationsSummary'
show(object)
## S4 method for signature 'PseudoDualSimulationsSummary'
show(object)

Arguments

object

the PseudoDualSimulationsSummary object we want to print

Value

invisibly returns a data frame of the results with one row and appropriate column names

Examples


##If DLE and efficacy responses are considered in the simulations
##Specified your simulations when no samples are used
## we need a data object with doses >= 1:
data <- DataDual(doseGrid=seq(25,300,25),placebo=FALSE)
##First for the DLE model 
##The DLE model must be of 'ModelTox' (e.g 'LogisticIndepBeta') class 
DLEmodel <- LogisticIndepBeta(binDLE=c(1.05,1.8),
                              DLEweights=c(3,3),
                              DLEdose=c(25,300),
                              data=data)

##The efficacy model of 'ModelEff' (e.g 'Effloglog') class 
Effmodel<-Effloglog(Eff=c(1.223,2.513),Effdose=c(25,300),
                    nu=c(a=1,b=0.025),data=data,c=0)

##The escalation rule using the 'NextBestMaxGain' class
mynextbest<-NextBestMaxGain(DLEDuringTrialtarget=0.35,
                            DLEEndOfTrialtarget=0.3)


##The increments (see Increments class examples) 
## 200% allowable increase for dose below 300 and 200% increase for dose above 300
myIncrements<-IncrementsRelative(intervals=c(25,300),
                                 increments=c(2,2))
##cohort size of 3
mySize<-CohortSizeConst(size=3)
##Stop only when 36 subjects are treated
myStopping <- StoppingMinPatients(nPatients=36)
##Now specified the design with all the above information and starting with a dose of 25

##Specified the design(for details please refer to the 'DualResponsesDesign' example)
design <- DualResponsesDesign(nextBest=mynextbest,
                              model=DLEmodel,
                              Effmodel=Effmodel,
                              stopping=myStopping,
                              increments=myIncrements,
                              cohortSize=mySize,
                              data=data,startingDose=25)
##Specify the true DLE and efficacy curves
myTruthDLE<- function(dose)
{ DLEmodel@prob(dose, phi1=-53.66584, phi2=10.50499)
}

myTruthEff<- function(dose)
{Effmodel@ExpEff(dose,theta1=-4.818429,theta2=3.653058)
}

## Then specified the simulations and generate the trial 
##For illustration purpose only 2 simulation is produced (nsim=2). 
mySim <-simulate(object=design,
                 args=NULL,
                 trueDLE=myTruthDLE,
                 trueEff=myTruthEff,
                 trueNu=1/0.025,
                 nsim=2,
                 seed=819,
                 parallel=FALSE)
##Then produce a summary of your simulations
MYSUM <- summary(mySim,
                 trueDLE=myTruthDLE,
                 trueEff=myTruthEff)
##Then show the summary in data frame for your simulations
show(MYSUM)




##If DLE and efficacy samples are involved
##The escalation rule using the 'NextBestMaxGainSamples' class
mynextbest<-NextBestMaxGainSamples(DLEDuringTrialtarget=0.35,
                                   DLEEndOfTrialtarget=0.3,
                                   TDderive=function(TDsamples){
                                     quantile(TDsamples,prob=0.3)},
                                   Gstarderive=function(Gstarsamples){
                                     quantile(Gstarsamples,prob=0.5)})
##The design of 'DualResponsesSamplesDesign' class
design <- DualResponsesSamplesDesign(nextBest=mynextbest,
                                     cohortSize=mySize,
                                     startingDose=25,
                                     model=DLEmodel,
                                     Effmodel=Effmodel,
                                     data=data,
                                     stopping=myStopping,
                                     increments=myIncrements)
##options for MCMC
##for illustration purpose, we will use 50 burn-ins to generate 200 samples
options<-McmcOptions(burnin=50,step=2,samples=200)
##The simulations for illustration purpose we only simulate 2 trials (nsim=2)
mySim<-simulate(design,
                args=NULL,
                trueDLE=myTruthDLE,
                trueEff=myTruthEff,
                trueNu=1/0.025,
                nsim=2,
                mcmcOptions=options,
                seed=819,
                parallel=FALSE)


##Then produce a summary of your simulations
MYSUM <- summary(mySim,
                 trueDLE=myTruthDLE,
                trueEff=myTruthEff)
##Then show the summary in data frame for your simulations
show(MYSUM)
##If DLE and efficacy responses are considered in the simulations
##Specified your simulations when no samples are used
## we need a data object with doses >= 1:
data <- DataDual(doseGrid=seq(25,300,25),placebo=FALSE)
##First for the DLE model 
##The DLE model must be of 'ModelTox' (e.g 'LogisticIndepBeta') class 
DLEmodel <- LogisticIndepBeta(binDLE=c(1.05,1.8),
                              DLEweights=c(3,3),
                              DLEdose=c(25,300),
                              data=data)

##The efficacy model of 'ModelEff' (e.g 'Effloglog') class 
Effmodel<-Effloglog(Eff=c(1.223,2.513),Effdose=c(25,300),
                    nu=c(a=1,b=0.025),data=data,c=0)

##The escalation rule using the 'NextBestMaxGain' class
mynextbest<-NextBestMaxGain(DLEDuringTrialtarget=0.35,
                            DLEEndOfTrialtarget=0.3)


##The increments (see Increments class examples) 
## 200% allowable increase for dose below 300 and 200% increase for dose above 300
myIncrements<-IncrementsRelative(intervals=c(25,300),
                                 increments=c(2,2))
##cohort size of 3
mySize<-CohortSizeConst(size=3)
##Stop only when 36 subjects are treated
myStopping <- StoppingMinPatients(nPatients=36)
##Now specified the design with all the above information and starting with a dose of 25

##Specified the design(for details please refer to the 'DualResponsesDesign' example)
design <- DualResponsesDesign(nextBest=mynextbest,
                              model=DLEmodel,
                              Effmodel=Effmodel,
                              stopping=myStopping,
                              increments=myIncrements,
                              cohortSize=mySize,
                              data=data,startingDose=25)
##Specify the true DLE and efficacy curves
myTruthDLE<- function(dose)
{ DLEmodel@prob(dose, phi1=-53.66584, phi2=10.50499)
}

myTruthEff<- function(dose)
{Effmodel@ExpEff(dose,theta1=-4.818429,theta2=3.653058)
}

## Then specified the simulations and generate the trial 
##For illustration purpose only 2 simulation is produced (nsim=2). 
mySim <-simulate(object=design,
                 args=NULL,
                 trueDLE=myTruthDLE,
                 trueEff=myTruthEff,
                 trueNu=1/0.025,
                 nsim=2,
                 seed=819,
                 parallel=FALSE)
##Then produce a summary of your simulations
MYSUM <- summary(mySim,
                 trueDLE=myTruthDLE,
                 trueEff=myTruthEff)
##Then show the summary in data frame for your simulations
show(MYSUM)




##If DLE and efficacy samples are involved
##The escalation rule using the 'NextBestMaxGainSamples' class
mynextbest<-NextBestMaxGainSamples(DLEDuringTrialtarget=0.35,
                                   DLEEndOfTrialtarget=0.3,
                                   TDderive=function(TDsamples){
                                     quantile(TDsamples,prob=0.3)},
                                   Gstarderive=function(Gstarsamples){
                                     quantile(Gstarsamples,prob=0.5)})
##The design of 'DualResponsesSamplesDesign' class
design <- DualResponsesSamplesDesign(nextBest=mynextbest,
                                     cohortSize=mySize,
                                     startingDose=25,
                                     model=DLEmodel,
                                     Effmodel=Effmodel,
                                     data=data,
                                     stopping=myStopping,
                                     increments=myIncrements)
##options for MCMC
##for illustration purpose, we will use 50 burn-ins to generate 200 samples
options<-McmcOptions(burnin=50,step=2,samples=200)
##The simulations for illustration purpose we only simulate 2 trials (nsim=2)
mySim<-simulate(design,
                args=NULL,
                trueDLE=myTruthDLE,
                trueEff=myTruthEff,
                trueNu=1/0.025,
                nsim=2,
                mcmcOptions=options,
                seed=819,
                parallel=FALSE)


##Then produce a summary of your simulations
MYSUM <- summary(mySim,
                 trueDLE=myTruthDLE,
                trueEff=myTruthEff)
##Then show the summary in data frame for your simulations
show(MYSUM)

Show the summary of the simulations

Description

Show the summary of the simulations

Usage

## S4 method for signature 'PseudoSimulationsSummary'
show(object)
## S4 method for signature 'PseudoSimulationsSummary'
show(object)

Arguments

object

the PseudoSimulationsSummary object we want to print

Value

invisibly returns a data frame of the results with one row and appropriate column names

Examples

##obtain the plot for the simulation results
##If only DLE responses are considered in the simulations
##Specified your simulations when no DLE samples are used
data <- Data(doseGrid=seq(25,300,25))

##The design only incorporate DLE responses and DLE samples are involved
##Specified the model of 'ModelTox' class eg 'LogisticIndepBeta' class model
model<-LogisticIndepBeta(binDLE=c(1.05,1.8),DLEweights=c(3,3),DLEdose=c(25,300),data=data)
##Then the escalation rule
tdNextBest <- NextBestTD(targetDuringTrial=0.35,
                         targetEndOfTrial=0.3)

##Then the starting data, an empty data set
emptydata<-Data(doseGrid=seq(25,300,25))
## The cohort size, size of 3 subjects
mySize <-CohortSizeConst(size=3)
##Deifne the increments for the dose-escalation process
##The maximum increase of 200% for doses up to the maximum of the dose specified in the doseGrid
##The maximum increase of 200% for dose above the maximum of the dose specified in the doseGrid
##This is to specified a maximum of 3-fold restriction in dose-esclation
myIncrements<-IncrementsRelative(intervals=c(min(data@doseGrid),max(data@doseGrid)), 
                                 increments=c(2,2))
##Specified the stopping rule e.g stop when the maximum sample size of 36 patients has been reached
myStopping <- StoppingMinPatients(nPatients=36)


##Specified the design(for details please refer to the 'TDDesign' example)
design <- TDDesign(model=model,
                   nextBest=tdNextBest,
                   stopping=myStopping,
                   increments=myIncrements,
                   cohortSize=mySize,
                   data=data,startingDose=25)

##Specify the truth of the DLE responses
myTruth <- function(dose)
{ model@prob(dose, phi1=-53.66584, phi2=10.50499)
}

##The simulations
##For illustration purpose only 1 simulation is produced (nsim=1). 
mySim <- simulate(design,
                  args=NULL,
                  truth=myTruth,
                  nsim=1,
                  seed=819,
                  parallel=FALSE)
##Then produce a summary of your simulations
MYSUM <- summary(mySim,
                 truth=myTruth)
##show the summary of the simulated results in a data frame
show(MYSUM)



##If DLE samples are involved
##The escalation rule
tdNextBest<-NextBestTDsamples(targetDuringTrial=0.35,
                              targetEndOfTrial=0.3,
                              derive=function(TDsamples){quantile(TDsamples,probs=0.3)})
##The design
design <- TDsamplesDesign(model=model,
                          nextBest=tdNextBest,
                          stopping=myStopping,
                          increments=myIncrements,
                          cohortSize=mySize,
                          data=data,startingDose=25)
##Options for MCMC
##For illustration purpose, we will use 50 burn-ins to generate 200 samples and 
##only simulate for 2 trials (nsim=2)
options<-McmcOptions(burnin=50,step=2,samples=200)
##The simualtions
mySim <- simulate(design,
                  args=NULL,
                  truth=myTruth,
                  nsim=2,
                  seed=819,
                  mcmcOptions=options,
                  parallel=FALSE)
##Then produce a summary of your simulations
MYSUM <- summary(mySim,
         truth=myTruth)
##show the summary of the simulated results in a data frame
show(MYSUM)
##obtain the plot for the simulation results
##If only DLE responses are considered in the simulations
##Specified your simulations when no DLE samples are used
data <- Data(doseGrid=seq(25,300,25))

##The design only incorporate DLE responses and DLE samples are involved
##Specified the model of 'ModelTox' class eg 'LogisticIndepBeta' class model
model<-LogisticIndepBeta(binDLE=c(1.05,1.8),DLEweights=c(3,3),DLEdose=c(25,300),data=data)
##Then the escalation rule
tdNextBest <- NextBestTD(targetDuringTrial=0.35,
                         targetEndOfTrial=0.3)

##Then the starting data, an empty data set
emptydata<-Data(doseGrid=seq(25,300,25))
## The cohort size, size of 3 subjects
mySize <-CohortSizeConst(size=3)
##Deifne the increments for the dose-escalation process
##The maximum increase of 200% for doses up to the maximum of the dose specified in the doseGrid
##The maximum increase of 200% for dose above the maximum of the dose specified in the doseGrid
##This is to specified a maximum of 3-fold restriction in dose-esclation
myIncrements<-IncrementsRelative(intervals=c(min(data@doseGrid),max(data@doseGrid)), 
                                 increments=c(2,2))
##Specified the stopping rule e.g stop when the maximum sample size of 36 patients has been reached
myStopping <- StoppingMinPatients(nPatients=36)


##Specified the design(for details please refer to the 'TDDesign' example)
design <- TDDesign(model=model,
                   nextBest=tdNextBest,
                   stopping=myStopping,
                   increments=myIncrements,
                   cohortSize=mySize,
                   data=data,startingDose=25)

##Specify the truth of the DLE responses
myTruth <- function(dose)
{ model@prob(dose, phi1=-53.66584, phi2=10.50499)
}

##The simulations
##For illustration purpose only 1 simulation is produced (nsim=1). 
mySim <- simulate(design,
                  args=NULL,
                  truth=myTruth,
                  nsim=1,
                  seed=819,
                  parallel=FALSE)
##Then produce a summary of your simulations
MYSUM <- summary(mySim,
                 truth=myTruth)
##show the summary of the simulated results in a data frame
show(MYSUM)



##If DLE samples are involved
##The escalation rule
tdNextBest<-NextBestTDsamples(targetDuringTrial=0.35,
                              targetEndOfTrial=0.3,
                              derive=function(TDsamples){quantile(TDsamples,probs=0.3)})
##The design
design <- TDsamplesDesign(model=model,
                          nextBest=tdNextBest,
                          stopping=myStopping,
                          increments=myIncrements,
                          cohortSize=mySize,
                          data=data,startingDose=25)
##Options for MCMC
##For illustration purpose, we will use 50 burn-ins to generate 200 samples and 
##only simulate for 2 trials (nsim=2)
options<-McmcOptions(burnin=50,step=2,samples=200)
##The simualtions
mySim <- simulate(design,
                  args=NULL,
                  truth=myTruth,
                  nsim=2,
                  seed=819,
                  mcmcOptions=options,
                  parallel=FALSE)
##Then produce a summary of your simulations
MYSUM <- summary(mySim,
         truth=myTruth)
##show the summary of the simulated results in a data frame
show(MYSUM)

Show the summary of the simulations

Description

Show the summary of the simulations

Usage

## S4 method for signature 'SimulationsSummary'
show(object)
## S4 method for signature 'SimulationsSummary'
show(object)

Arguments

object

the SimulationsSummary object we want to print

Value

invisibly returns a data frame of the results with one row and appropriate column names

Examples


# Define the dose-grid
emptydata <- Data(doseGrid = c(1, 3, 5, 10, 15, 20, 25, 40, 50, 80, 100))

# Initialize the CRM model 
model <- LogisticLogNormal(mean=c(-0.85, 1),
                           cov=
                             matrix(c(1, -0.5, -0.5, 1),
                                    nrow=2),
                           refDose=56)

# Choose the rule for selecting the next dose 
myNextBest <- NextBestNCRM(target=c(0.2, 0.35),
                           overdose=c(0.35, 1),
                           maxOverdoseProb=0.25)

# Choose the rule for the cohort-size 
mySize1 <- CohortSizeRange(intervals=c(0, 30),
                           cohortSize=c(1, 3))
mySize2 <- CohortSizeDLT(DLTintervals=c(0, 1),
                         cohortSize=c(1, 3))
mySize <- maxSize(mySize1, mySize2)

# Choose the rule for stopping
myStopping1 <- StoppingMinCohorts(nCohorts=3)
myStopping2 <- StoppingTargetProb(target=c(0.2, 0.35),
                                  prob=0.5)
myStopping3 <- StoppingMinPatients(nPatients=20)
myStopping <- (myStopping1 & myStopping2) | myStopping3

# Choose the rule for dose increments
myIncrements <- IncrementsRelative(intervals=c(0, 20),
                                   increments=c(1, 0.33))

# Initialize the design
design <- Design(model=model,
                 nextBest=myNextBest,
                 stopping=myStopping,
                 increments=myIncrements,
                 cohortSize=mySize,
                 data=emptydata,
                 startingDose=3)

## define the true function
myTruth <- function(dose)
{
  model@prob(dose, alpha0=7, alpha1=8)
}

# Run the simulation on the desired design
# We only generate 1 trial outcome here for illustration, for the actual study 
# this should be increased of course
options <- McmcOptions(burnin=100,
                       step=2,
                       samples=1000)
time <- system.time(mySims <- simulate(design,
                                       args=NULL,
                                       truth=myTruth,
                                       nsim=1,
                                       seed=819,
                                       mcmcOptions=options,
                                       parallel=FALSE))[3]

# Show the Summary of the Simulations
show(summary(mySims,truth=myTruth))


# Define the dose-grid
emptydata <- Data(doseGrid = c(1, 3, 5, 10, 15, 20, 25, 40, 50, 80, 100))

# Initialize the CRM model 
model <- LogisticLogNormal(mean=c(-0.85, 1),
                           cov=
                             matrix(c(1, -0.5, -0.5, 1),
                                    nrow=2),
                           refDose=56)

# Choose the rule for selecting the next dose 
myNextBest <- NextBestNCRM(target=c(0.2, 0.35),
                           overdose=c(0.35, 1),
                           maxOverdoseProb=0.25)

# Choose the rule for the cohort-size 
mySize1 <- CohortSizeRange(intervals=c(0, 30),
                           cohortSize=c(1, 3))
mySize2 <- CohortSizeDLT(DLTintervals=c(0, 1),
                         cohortSize=c(1, 3))
mySize <- maxSize(mySize1, mySize2)

# Choose the rule for stopping
myStopping1 <- StoppingMinCohorts(nCohorts=3)
myStopping2 <- StoppingTargetProb(target=c(0.2, 0.35),
                                  prob=0.5)
myStopping3 <- StoppingMinPatients(nPatients=20)
myStopping <- (myStopping1 & myStopping2) | myStopping3

# Choose the rule for dose increments
myIncrements <- IncrementsRelative(intervals=c(0, 20),
                                   increments=c(1, 0.33))

# Initialize the design
design <- Design(model=model,
                 nextBest=myNextBest,
                 stopping=myStopping,
                 increments=myIncrements,
                 cohortSize=mySize,
                 data=emptydata,
                 startingDose=3)

## define the true function
myTruth <- function(dose)
{
  model@prob(dose, alpha0=7, alpha1=8)
}

# Run the simulation on the desired design
# We only generate 1 trial outcome here for illustration, for the actual study 
# this should be increased of course
options <- McmcOptions(burnin=100,
                       step=2,
                       samples=1000)
time <- system.time(mySims <- simulate(design,
                                       args=NULL,
                                       truth=myTruth,
                                       nsim=1,
                                       seed=819,
                                       mcmcOptions=options,
                                       parallel=FALSE))[3]

# Show the Summary of the Simulations
show(summary(mySims,truth=myTruth))

Simulate outcomes from a CRM design

Description

Simulate outcomes from a CRM design

Usage

## S4 method for signature 'Design'
simulate(
  object,
  nsim = 1L,
  seed = NULL,
  truth,
  args = NULL,
  firstSeparate = FALSE,
  mcmcOptions = McmcOptions(),
  parallel = FALSE,
  nCores = min(parallel::detectCores(), 5),
  ...
)
## S4 method for signature 'Design'
simulate(
  object,
  nsim = 1L,
  seed = NULL,
  truth,
  args = NULL,
  firstSeparate = FALSE,
  mcmcOptions = McmcOptions(),
  parallel = FALSE,
  nCores = min(parallel::detectCores(), 5),
  ...
)

Arguments

`object`	the `Design` object we want to simulate data from
`nsim`	the number of simulations (default: 1)
`seed`	see `setSeed`
`truth`	a function which takes as input a dose (vector) and returns the true probability (vector) for toxicity. Additional arguments can be supplied in `args`.
`args`	data frame with arguments for the `truth` function. The column names correspond to the argument names, the rows to the values of the arguments. The rows are appropriately recycled in the `nsim` simulations. In order to produce outcomes from the posterior predictive distribution, e.g, pass an `object` that contains the data observed so far, `truth` contains the `prob` function from the model in `object`, and `args` contains posterior samples from the model.
`firstSeparate`	enroll the first patient separately from the rest of the cohort? (not default) If yes, the cohort will be closed if a DLT occurs in this patient.
`mcmcOptions`	object of class `McmcOptions`, giving the MCMC options for each evaluation in the trial. By default, the standard options are used
`parallel`	should the simulation runs be parallelized across the clusters of the computer? (not default)
`nCores`	how many cores should be used for parallel computing? Defaults to the number of cores on the machine, maximum 5.
`...`	not used

Value

an object of class Simulations

Examples


# Define the dose-grid
emptydata <- Data(doseGrid = c(1, 3, 5, 10, 15, 20, 25, 40, 50, 80, 100))

# Initialize the CRM model 
model <- LogisticLogNormal(mean=c(-0.85, 1),
                           cov=
                             matrix(c(1, -0.5, -0.5, 1),
                                    nrow=2),
                           refDose=56)

# Choose the rule for selecting the next dose 
myNextBest <- NextBestNCRM(target=c(0.2, 0.35),
                           overdose=c(0.35, 1),
                           maxOverdoseProb=0.25)

# Choose the rule for the cohort-size 
mySize1 <- CohortSizeRange(intervals=c(0, 30),
                           cohortSize=c(1, 3))
mySize2 <- CohortSizeDLT(DLTintervals=c(0, 1),
                         cohortSize=c(1, 3))
mySize <- maxSize(mySize1, mySize2)

# Choose the rule for stopping
myStopping1 <- StoppingMinCohorts(nCohorts=3)
myStopping2 <- StoppingTargetProb(target=c(0.2, 0.35),
                                  prob=0.5)
myStopping3 <- StoppingMinPatients(nPatients=20)
myStopping <- (myStopping1 & myStopping2) | myStopping3

# Choose the rule for dose increments
myIncrements <- IncrementsRelative(intervals=c(0, 20),
                                   increments=c(1, 0.33))

# Initialize the design
design <- Design(model=model,
                 nextBest=myNextBest,
                 stopping=myStopping,
                 increments=myIncrements,
                 cohortSize=mySize,
                 data=emptydata,
                 startingDose=3)

## define the true function
myTruth <- function(dose)
{
  model@prob(dose, alpha0=7, alpha1=8)
}

# Run the simulation on the desired design
# We only generate 1 trial outcomes here for illustration, for the actual study 
# this should be increased of course
options <- McmcOptions(burnin=100,
                       step=1,
                       samples=2000)
time <- system.time(mySims <- simulate(design,
                                       args=NULL,
                                       truth=myTruth,
                                       nsim=1,
                                       seed=819,
                                       mcmcOptions=options,
                                       parallel=FALSE))[3]
  
  
# Define the dose-grid
emptydata <- Data(doseGrid = c(1, 3, 5, 10, 15, 20, 25, 40, 50, 80, 100))

# Initialize the CRM model 
model <- LogisticLogNormal(mean=c(-0.85, 1),
                           cov=
                             matrix(c(1, -0.5, -0.5, 1),
                                    nrow=2),
                           refDose=56)

# Choose the rule for selecting the next dose 
myNextBest <- NextBestNCRM(target=c(0.2, 0.35),
                           overdose=c(0.35, 1),
                           maxOverdoseProb=0.25)

# Choose the rule for the cohort-size 
mySize1 <- CohortSizeRange(intervals=c(0, 30),
                           cohortSize=c(1, 3))
mySize2 <- CohortSizeDLT(DLTintervals=c(0, 1),
                         cohortSize=c(1, 3))
mySize <- maxSize(mySize1, mySize2)

# Choose the rule for stopping
myStopping1 <- StoppingMinCohorts(nCohorts=3)
myStopping2 <- StoppingTargetProb(target=c(0.2, 0.35),
                                  prob=0.5)
myStopping3 <- StoppingMinPatients(nPatients=20)
myStopping <- (myStopping1 & myStopping2) | myStopping3

# Choose the rule for dose increments
myIncrements <- IncrementsRelative(intervals=c(0, 20),
                                   increments=c(1, 0.33))

# Initialize the design
design <- Design(model=model,
                 nextBest=myNextBest,
                 stopping=myStopping,
                 increments=myIncrements,
                 cohortSize=mySize,
                 data=emptydata,
                 startingDose=3)

## define the true function
myTruth <- function(dose)
{
  model@prob(dose, alpha0=7, alpha1=8)
}

# Run the simulation on the desired design
# We only generate 1 trial outcomes here for illustration, for the actual study 
# this should be increased of course
options <- McmcOptions(burnin=100,
                       step=1,
                       samples=2000)
time <- system.time(mySims <- simulate(design,
                                       args=NULL,
                                       truth=myTruth,
                                       nsim=1,
                                       seed=819,
                                       mcmcOptions=options,
                                       parallel=FALSE))[3]

Simulate outcomes from a dual-endpoint design

Description

Simulate outcomes from a dual-endpoint design

Usage

## S4 method for signature 'DualDesign'
simulate(
  object,
  nsim = 1L,
  seed = NULL,
  trueTox,
  trueBiomarker,
  args = NULL,
  sigma2W,
  rho = 0,
  firstSeparate = FALSE,
  mcmcOptions = McmcOptions(),
  parallel = FALSE,
  nCores = min(parallel::detectCores(), 5),
  ...
)
## S4 method for signature 'DualDesign'
simulate(
  object,
  nsim = 1L,
  seed = NULL,
  trueTox,
  trueBiomarker,
  args = NULL,
  sigma2W,
  rho = 0,
  firstSeparate = FALSE,
  mcmcOptions = McmcOptions(),
  parallel = FALSE,
  nCores = min(parallel::detectCores(), 5),
  ...
)

Arguments

`object`	the `DualDesign` object we want to simulate data from
`nsim`	the number of simulations (default: 1)
`seed`	see `setSeed`
`trueTox`	a function which takes as input a dose (vector) and returns the true probability (vector) for toxicity. Additional arguments can be supplied in `args`.
`trueBiomarker`	a function which takes as input a dose (vector) and returns the true biomarker level (vector). Additional arguments can be supplied in `args`.
`args`	data frame with arguments for the `trueTox` and `trueBiomarker` function. The column names correspond to the argument names, the rows to the values of the arguments. The rows are appropriately recycled in the `nsim` simulations.
`sigma2W`	variance for the biomarker measurements
`rho`	correlation between toxicity and biomarker measurements (default: 0)
`firstSeparate`	enroll the first patient separately from the rest of the cohort? (not default) If yes, the cohort will be closed if a DLT occurs in this patient.
`mcmcOptions`	object of class `McmcOptions`, giving the MCMC options for each evaluation in the trial. By default, the standard options are used
`parallel`	should the simulation runs be parallelized across the clusters of the computer? (not default)
`nCores`	how many cores should be used for parallel computing? Defaults to the number of cores on the machine, maximum 5.
`...`	not used

Value

an object of class DualSimulations

Examples


# Define the dose-grid
emptydata <- DataDual(doseGrid = c(1, 3, 5, 10, 15, 20, 25, 40, 50, 80, 100))

# Initialize the CRM model 
model <- DualEndpointRW(mu = c(0, 1),
                        Sigma = matrix(c(1, 0, 0, 1), nrow=2),
                        sigma2betaW = 0.01,
                        sigma2W = c(a=0.1, b=0.1),
                        useLogDose=TRUE,
                        refDose=2,
                        rho = c(a=1, b=1),
                        smooth="RW1")

# Choose the rule for selecting the next dose 
myNextBest <- NextBestDualEndpoint(target=c(0.9, 1),
                                   overdose=c(0.35, 1),
                                   maxOverdoseProb=0.25)

# Choose the rule for the cohort-size 
mySize1 <- CohortSizeRange(intervals=c(0, 30),
                           cohortSize=c(1, 3))
mySize2 <- CohortSizeDLT(DLTintervals=c(0, 1),
                         cohortSize=c(1, 3))
mySize <- maxSize(mySize1, mySize2)

# Choose the rule for stopping
myStopping4 <- StoppingTargetBiomarker(target=c(0.9, 1),
                                       prob=0.5)
myStopping <- myStopping4 | StoppingMinPatients(10)

# Choose the rule for dose increments
myIncrements <- IncrementsRelative(intervals=c(0, 20),
                                   increments=c(1, 0.33))

# Initialize the design
design <- DualDesign(model = model,
                     data = emptydata,
                     nextBest = myNextBest,
                     stopping = myStopping,
                     increments = myIncrements,
                     cohortSize = mySize,
                     startingDose = 3)
  
# define scenarios for the TRUE toxicity and efficacy profiles
betaMod <- function (dose, e0, eMax, delta1, delta2, scal)
{
  maxDens <- (delta1^delta1) * (delta2^delta2)/((delta1 + delta2)^(delta1 + delta2))
  dose <- dose/scal
  e0 + eMax/maxDens * (dose^delta1) * (1 - dose)^delta2
}

trueBiomarker <- function(dose)
{
  betaMod(dose, e0=0.2, eMax=0.6, delta1=5, delta2=5 * 0.5 / 0.5, scal=100)
}

trueTox <- function(dose)
{
  pnorm((dose-60)/10)
}

# Draw the TRUE profiles
par(mfrow=c(1, 2))
curve(trueTox(x), from=0, to=80)
curve(trueBiomarker(x), from=0, to=80)

# Run the simulation on the desired design
# We only generate 1 trial outcome here for illustration, for the actual study 
# this should be increased of course, similarly for the McmcOptions - 
# they also need to be increased.
mySims <- simulate(design,
                   trueTox=trueTox,
                   trueBiomarker=trueBiomarker,
                   sigma2W=0.01,
                   rho=0,
                   nsim=1,
                   parallel=FALSE,
                   seed=3,
                   startingDose=6,
                   mcmcOptions =
                     McmcOptions(burnin=100,
                                 step=1,
                                 samples=300))
  
# Define the dose-grid
emptydata <- DataDual(doseGrid = c(1, 3, 5, 10, 15, 20, 25, 40, 50, 80, 100))

# Initialize the CRM model 
model <- DualEndpointRW(mu = c(0, 1),
                        Sigma = matrix(c(1, 0, 0, 1), nrow=2),
                        sigma2betaW = 0.01,
                        sigma2W = c(a=0.1, b=0.1),
                        useLogDose=TRUE,
                        refDose=2,
                        rho = c(a=1, b=1),
                        smooth="RW1")

# Choose the rule for selecting the next dose 
myNextBest <- NextBestDualEndpoint(target=c(0.9, 1),
                                   overdose=c(0.35, 1),
                                   maxOverdoseProb=0.25)

# Choose the rule for the cohort-size 
mySize1 <- CohortSizeRange(intervals=c(0, 30),
                           cohortSize=c(1, 3))
mySize2 <- CohortSizeDLT(DLTintervals=c(0, 1),
                         cohortSize=c(1, 3))
mySize <- maxSize(mySize1, mySize2)

# Choose the rule for stopping
myStopping4 <- StoppingTargetBiomarker(target=c(0.9, 1),
                                       prob=0.5)
myStopping <- myStopping4 | StoppingMinPatients(10)

# Choose the rule for dose increments
myIncrements <- IncrementsRelative(intervals=c(0, 20),
                                   increments=c(1, 0.33))

# Initialize the design
design <- DualDesign(model = model,
                     data = emptydata,
                     nextBest = myNextBest,
                     stopping = myStopping,
                     increments = myIncrements,
                     cohortSize = mySize,
                     startingDose = 3)
  
# define scenarios for the TRUE toxicity and efficacy profiles
betaMod <- function (dose, e0, eMax, delta1, delta2, scal)
{
  maxDens <- (delta1^delta1) * (delta2^delta2)/((delta1 + delta2)^(delta1 + delta2))
  dose <- dose/scal
  e0 + eMax/maxDens * (dose^delta1) * (1 - dose)^delta2
}

trueBiomarker <- function(dose)
{
  betaMod(dose, e0=0.2, eMax=0.6, delta1=5, delta2=5 * 0.5 / 0.5, scal=100)
}

trueTox <- function(dose)
{
  pnorm((dose-60)/10)
}

# Draw the TRUE profiles
par(mfrow=c(1, 2))
curve(trueTox(x), from=0, to=80)
curve(trueBiomarker(x), from=0, to=80)

# Run the simulation on the desired design
# We only generate 1 trial outcome here for illustration, for the actual study 
# this should be increased of course, similarly for the McmcOptions - 
# they also need to be increased.
mySims <- simulate(design,
                   trueTox=trueTox,
                   trueBiomarker=trueBiomarker,
                   sigma2W=0.01,
                   rho=0,
                   nsim=1,
                   parallel=FALSE,
                   seed=3,
                   startingDose=6,
                   mcmcOptions =
                     McmcOptions(burnin=100,
                                 step=1,
                                 samples=300))

This is a methods to simulate dose escalation procedure using both DLE and efficacy responses. This is a method based on the `DualResponsesDesign` where DLEmodel used are of `ModelTox` class object and efficacy model used are of `ModelEff` class object. In addition, no DLE and efficacy samples are involved or generated in the simulation process

Description

This is a methods to simulate dose escalation procedure using both DLE and efficacy responses. This is a method based on the DualResponsesDesign where DLEmodel used are of ModelTox class object and efficacy model used are of ModelEff class object. In addition, no DLE and efficacy samples are involved or generated in the simulation process

Usage

## S4 method for signature 'DualResponsesDesign'
simulate(
  object,
  nsim = 1L,
  seed = NULL,
  trueDLE,
  trueEff,
  trueNu,
  args = NULL,
  firstSeparate = FALSE,
  parallel = FALSE,
  nCores = min(parallel::detectCores(), 5),
  ...
)
## S4 method for signature 'DualResponsesDesign'
simulate(
  object,
  nsim = 1L,
  seed = NULL,
  trueDLE,
  trueEff,
  trueNu,
  args = NULL,
  firstSeparate = FALSE,
  parallel = FALSE,
  nCores = min(parallel::detectCores(), 5),
  ...
)

Arguments

`object`	the `DualResponsesDesign` object we want to simulate the data from
`nsim`	the number of simulations (default :1)
`seed`	see `setSeed`
`trueDLE`	a function which takes as input a dose (vector) and returns the true probability (vector) of the occurrence of a DLE. Additional arguments can be supplied in `args`.
`trueEff`	a function which takes as input a dose (vector) and returns the expected efficacy responses (vector). Additional arguments can be supplied in `args`.
`trueNu`	the precision, the inverse of the variance of the efficacy responses
`args`	data frame with arguments for the `trueDLE` and `trueEff` function. The column names correspond to the argument names, the rows to the values of the arguments. The rows are appropriately recycled in the `nsim` simulations.
`firstSeparate`	enroll the first patient separately from the rest of the cohort? (not default) If yes, the cohort will be closed if a DLT occurs in this patient.
`parallel`	should the simulation runs be parallelized across the clusters of the computer? (not default)
`nCores`	how many cores should be used for parallel computing? Defaults to the number of cores on the machine, maximum 5.
`...`	not used

Value

an object of class PseudoDualSimulations

Examples

##Simulate dose-escalation procedure based on DLE and efficacy responses where no DLE 
## and efficacy samples are used
## we need a data object with doses >= 1:
data <- DataDual(doseGrid=seq(25,300,25),placebo=FALSE)
##First for the DLE model 
##The DLE model must be of 'ModelTox' (e.g 'LogisticIndepBeta') class 
DLEmodel <- LogisticIndepBeta(binDLE=c(1.05,1.8),
                              DLEweights=c(3,3),
                              DLEdose=c(25,300),
                              data=data)

##The efficacy model of 'ModelEff' (e.g 'Effloglog') class 
Effmodel<-Effloglog(Eff=c(1.223,2.513),Effdose=c(25,300),
                    nu=c(a=1,b=0.025),data=data,c=0)

##The escalation rule using the 'NextBestMaxGain' class
mynextbest<-NextBestMaxGain(DLEDuringTrialtarget=0.35,
                            DLEEndOfTrialtarget=0.3)


##The increments (see Increments class examples) 
## 200% allowable increase for dose below 300 and 200% increase for dose above 300
myIncrements<-IncrementsRelative(intervals=c(25,300),
                                 increments=c(2,2))
##cohort size of 3
mySize<-CohortSizeConst(size=3)
##Stop only when 36 subjects are treated
myStopping <- StoppingMinPatients(nPatients=36)
##Now specified the design with all the above information and starting with a dose of 25

##Specified the design(for details please refer to the 'DualResponsesDesign' example)
design <- DualResponsesDesign(nextBest=mynextbest,
                              model=DLEmodel,
                              Effmodel=Effmodel,
                              stopping=myStopping,
                              increments=myIncrements,
                              cohortSize=mySize,
                              data=data,startingDose=25)
##Specify the true DLE and efficacy curves
myTruthDLE<- function(dose)
{ DLEmodel@prob(dose, phi1=-53.66584, phi2=10.50499)
}

myTruthEff<- function(dose)
{Effmodel@ExpEff(dose,theta1=-4.818429,theta2=3.653058)
}

##The true gain curve can also be seen
myTruthGain <- function(dose)
{return((myTruthEff(dose))/(1+(myTruthDLE(dose)/(1-myTruthDLE(dose)))))}


## Then specified the simulations and generate the trial 
##For illustration purpose only 1 simulation is produced (nsim=1). 
options<-McmcOptions(burnin=100,step=2,samples=200)
mySim <-simulate(object=design,
                 args=NULL,
                 trueDLE=myTruthDLE,
                 trueEff=myTruthEff,
                 trueNu=1/0.025,
                 nsim=1,
                 seed=819,
                 parallel=FALSE)
##Simulate dose-escalation procedure based on DLE and efficacy responses where no DLE 
## and efficacy samples are used
## we need a data object with doses >= 1:
data <- DataDual(doseGrid=seq(25,300,25),placebo=FALSE)
##First for the DLE model 
##The DLE model must be of 'ModelTox' (e.g 'LogisticIndepBeta') class 
DLEmodel <- LogisticIndepBeta(binDLE=c(1.05,1.8),
                              DLEweights=c(3,3),
                              DLEdose=c(25,300),
                              data=data)

##The efficacy model of 'ModelEff' (e.g 'Effloglog') class 
Effmodel<-Effloglog(Eff=c(1.223,2.513),Effdose=c(25,300),
                    nu=c(a=1,b=0.025),data=data,c=0)

##The escalation rule using the 'NextBestMaxGain' class
mynextbest<-NextBestMaxGain(DLEDuringTrialtarget=0.35,
                            DLEEndOfTrialtarget=0.3)


##The increments (see Increments class examples) 
## 200% allowable increase for dose below 300 and 200% increase for dose above 300
myIncrements<-IncrementsRelative(intervals=c(25,300),
                                 increments=c(2,2))
##cohort size of 3
mySize<-CohortSizeConst(size=3)
##Stop only when 36 subjects are treated
myStopping <- StoppingMinPatients(nPatients=36)
##Now specified the design with all the above information and starting with a dose of 25

##Specified the design(for details please refer to the 'DualResponsesDesign' example)
design <- DualResponsesDesign(nextBest=mynextbest,
                              model=DLEmodel,
                              Effmodel=Effmodel,
                              stopping=myStopping,
                              increments=myIncrements,
                              cohortSize=mySize,
                              data=data,startingDose=25)
##Specify the true DLE and efficacy curves
myTruthDLE<- function(dose)
{ DLEmodel@prob(dose, phi1=-53.66584, phi2=10.50499)
}

myTruthEff<- function(dose)
{Effmodel@ExpEff(dose,theta1=-4.818429,theta2=3.653058)
}

##The true gain curve can also be seen
myTruthGain <- function(dose)
{return((myTruthEff(dose))/(1+(myTruthDLE(dose)/(1-myTruthDLE(dose)))))}


## Then specified the simulations and generate the trial 
##For illustration purpose only 1 simulation is produced (nsim=1). 
options<-McmcOptions(burnin=100,step=2,samples=200)
mySim <-simulate(object=design,
                 args=NULL,
                 trueDLE=myTruthDLE,
                 trueEff=myTruthEff,
                 trueNu=1/0.025,
                 nsim=1,
                 seed=819,
                 parallel=FALSE)

This is a methods to simulate dose escalation procedure using both DLE and efficacy responses. This is a method based on the `DualResponsesSamplesDesign` where DLEmodel used are of `ModelTox` class object and efficacy model used are of `ModelEff` class object (special case is `EffFlexi` class model object). In addition, DLE and efficacy samples are involved or generated in the simulation process

Description

This is a methods to simulate dose escalation procedure using both DLE and efficacy responses. This is a method based on the DualResponsesSamplesDesign where DLEmodel used are of ModelTox class object and efficacy model used are of ModelEff class object (special case is EffFlexi class model object). In addition, DLE and efficacy samples are involved or generated in the simulation process

Usage

## S4 method for signature 'DualResponsesSamplesDesign'
simulate(
  object,
  nsim = 1L,
  seed = NULL,
  trueDLE,
  trueEff,
  trueNu = NULL,
  trueSigma2 = NULL,
  trueSigma2betaW = NULL,
  args = NULL,
  firstSeparate = FALSE,
  mcmcOptions = McmcOptions(),
  parallel = FALSE,
  nCores = min(parallel::detectCores(), 5),
  ...
)
## S4 method for signature 'DualResponsesSamplesDesign'
simulate(
  object,
  nsim = 1L,
  seed = NULL,
  trueDLE,
  trueEff,
  trueNu = NULL,
  trueSigma2 = NULL,
  trueSigma2betaW = NULL,
  args = NULL,
  firstSeparate = FALSE,
  mcmcOptions = McmcOptions(),
  parallel = FALSE,
  nCores = min(parallel::detectCores(), 5),
  ...
)

Arguments

`object`	the `DualResponsesSamplesDesign` object we want to simulate the data from
`nsim`	the number of simulations (default :1)
`seed`	see `setSeed`
`trueDLE`	a function which takes as input a dose (vector) and returns the true probability (vector) of the occurrence of a DLE. Additional arguments can be supplied in `args`.
`trueEff`	a function which takes as input a dose (vector) and returns the expected efficacy responses (vector). Additional arguments can be supplied in `args`.
`trueNu`	(not with `EffFlexi`) the precision, the inverse of the variance of the efficacy responses
`trueSigma2`	(only with `EffFlexi`) the true variance of the efficacy responses which must be a single positive scalar.
`trueSigma2betaW`	(only with `EffFlexi`) the true variance for the random walk model used for smoothing. This must be a single postive scalar.
`args`	data frame with arguments for the `trueDLE` and `trueEff` function. The column names correspond to the argument names, the rows to the values of the arguments. The rows are appropriately recycled in the `nsim` simulations.
`firstSeparate`	enroll the first patient separately from the rest of the cohort? (not default) If yes, the cohort will be closed if a DLT occurs in this patient.
`mcmcOptions`	object of class `McmcOptions`, giving the MCMC options for each evaluation in the trial. By default, the standard options are used
`parallel`	should the simulation runs be parallelized across the clusters of the computer? (not default)
`nCores`	how many cores should be used for parallel computing? Defaults to the number of cores on the machine, maximum 5.
`...`	not used

Value

an object of class PseudoDualSimulations or PseudoDualFlexiSimulations

Examples

##Simulate dose-escalation procedure based on DLE and efficacy responses where DLE 
## and efficacy samples are used

data <- DataDual(doseGrid=seq(25,300,25),placebo=FALSE)
##First for the DLE model 
##The DLE model must be of 'ModelTox' (e.g 'LogisticIndepBeta') class 
DLEmodel <- LogisticIndepBeta(binDLE=c(1.05,1.8),
                              DLEweights=c(3,3),
                              DLEdose=c(25,300),
                              data=data)

##The efficacy model of 'ModelEff' (e.g 'Effloglog') class 
Effmodel<-Effloglog(Eff=c(1.223,2.513),Effdose=c(25,300),
                    nu=c(a=1,b=0.025),data=data,c=0)


##The escalation rule using the 'NextBestMaxGainSamples' class
mynextbest<-NextBestMaxGainSamples(DLEDuringTrialtarget=0.35,
                                   DLEEndOfTrialtarget=0.3,
                                   TDderive=function(TDsamples){
                                     quantile(TDsamples,prob=0.3)},
                                   Gstarderive=function(Gstarsamples){
                                     quantile(Gstarsamples,prob=0.5)})


##The increments (see Increments class examples) 
## 200% allowable increase for dose below 300 and 200% increase for dose above 300
myIncrements<-IncrementsRelative(intervals=c(25,300),
                                 increments=c(2,2))
##cohort size of 3
mySize<-CohortSizeConst(size=3)
##Stop only when 10 subjects are treated (only for illustration such a low 
##sample size)
myStopping <- StoppingMinPatients(nPatients=10)
##Now specified the design with all the above information and starting with 
##a dose of 25


##Specified the design 
design <- DualResponsesSamplesDesign(nextBest=mynextbest,
                                     cohortSize=mySize,
                                     startingDose=25,
                                     model=DLEmodel,
                                     Effmodel=Effmodel,
                                     data=data,
                                     stopping=myStopping,
                                     increments=myIncrements)
##specified the true DLE and efficacy curve
myTruthDLE<- function(dose)
{ DLEmodel@prob(dose, phi1=-53.66584, phi2=10.50499)
}


myTruthEff<- function(dose)
{Effmodel@ExpEff(dose,theta1=-4.818429,theta2=3.653058)
}
##The true gain curve can also be seen
myTruthGain <- function(dose)
{return((myTruthEff(dose))/(1+(myTruthDLE(dose)/(1-myTruthDLE(dose)))))}

##simulate the trial for 10 times involving samples
##for illustration purpose we use 10 burn-ins to generate 50 samples
options<-McmcOptions(burnin=10,step=1,samples=50)
##For illustration purpose only 1 simulations are produced (nsim=1). 
mySim<-simulate(design,
                 args=NULL,
                 trueDLE=myTruthDLE,
                 trueEff=myTruthEff,
                 trueNu=1/0.025,
                 nsim=1,
                 mcmcOptions=options,
                 seed=819,
                 parallel=FALSE)



##Simulate dose-escalation procedure based on DLE and efficacy responses where DLE 
## and efficacy samples are used
## when the efficacy model is of 'EffFlexi' class
Effmodel<- EffFlexi(Eff=c(1.223, 2.513),Effdose=c(25,300),
                    sigma2=c(a=0.1,b=0.1),sigma2betaW=c(a=20,b=50),smooth="RW2",data=data)


##Specified the design 
design <- DualResponsesSamplesDesign(nextBest=mynextbest,
                                     cohortSize=mySize,
                                     startingDose=25,
                                     model=DLEmodel,
                                     Effmodel=Effmodel,
                                     data=data,
                                     stopping=myStopping,
                                     increments=myIncrements)
##specified the true DLE curve and the true expected efficacy values at all dose levels
myTruthDLE<- function(dose)
{ DLEmodel@prob(dose, phi1=-53.66584, phi2=10.50499)
}

myTruthEff<- c(-0.5478867, 0.1645417,  0.5248031,  0.7604467,  
               0.9333009  ,1.0687031,  1.1793942 , 1.2726408 , 
               1.3529598 , 1.4233411 , 1.4858613 , 1.5420182)
##The true gain curve can also be seen
d1 <- data@doseGrid
myTruthGain <- (myTruthEff)/(1+(myTruthDLE(d1)/(1-myTruthDLE(d1))))


mySim<-simulate(object=design,
                args=NULL,
                trueDLE=myTruthDLE,
                trueEff=myTruthEff,
                trueSigma2=0.025,
                trueSigma2betaW=1,
                mcmcOptions=options,
                nsim=1,
                seed=819,
                parallel=FALSE)


##Simulate dose-escalation procedure based on DLE and efficacy responses where DLE 
## and efficacy samples are used

data <- DataDual(doseGrid=seq(25,300,25),placebo=FALSE)
##First for the DLE model 
##The DLE model must be of 'ModelTox' (e.g 'LogisticIndepBeta') class 
DLEmodel <- LogisticIndepBeta(binDLE=c(1.05,1.8),
                              DLEweights=c(3,3),
                              DLEdose=c(25,300),
                              data=data)

##The efficacy model of 'ModelEff' (e.g 'Effloglog') class 
Effmodel<-Effloglog(Eff=c(1.223,2.513),Effdose=c(25,300),
                    nu=c(a=1,b=0.025),data=data,c=0)


##The escalation rule using the 'NextBestMaxGainSamples' class
mynextbest<-NextBestMaxGainSamples(DLEDuringTrialtarget=0.35,
                                   DLEEndOfTrialtarget=0.3,
                                   TDderive=function(TDsamples){
                                     quantile(TDsamples,prob=0.3)},
                                   Gstarderive=function(Gstarsamples){
                                     quantile(Gstarsamples,prob=0.5)})


##The increments (see Increments class examples) 
## 200% allowable increase for dose below 300 and 200% increase for dose above 300
myIncrements<-IncrementsRelative(intervals=c(25,300),
                                 increments=c(2,2))
##cohort size of 3
mySize<-CohortSizeConst(size=3)
##Stop only when 10 subjects are treated (only for illustration such a low 
##sample size)
myStopping <- StoppingMinPatients(nPatients=10)
##Now specified the design with all the above information and starting with 
##a dose of 25


##Specified the design 
design <- DualResponsesSamplesDesign(nextBest=mynextbest,
                                     cohortSize=mySize,
                                     startingDose=25,
                                     model=DLEmodel,
                                     Effmodel=Effmodel,
                                     data=data,
                                     stopping=myStopping,
                                     increments=myIncrements)
##specified the true DLE and efficacy curve
myTruthDLE<- function(dose)
{ DLEmodel@prob(dose, phi1=-53.66584, phi2=10.50499)
}


myTruthEff<- function(dose)
{Effmodel@ExpEff(dose,theta1=-4.818429,theta2=3.653058)
}
##The true gain curve can also be seen
myTruthGain <- function(dose)
{return((myTruthEff(dose))/(1+(myTruthDLE(dose)/(1-myTruthDLE(dose)))))}

##simulate the trial for 10 times involving samples
##for illustration purpose we use 10 burn-ins to generate 50 samples
options<-McmcOptions(burnin=10,step=1,samples=50)
##For illustration purpose only 1 simulations are produced (nsim=1). 
mySim<-simulate(design,
                 args=NULL,
                 trueDLE=myTruthDLE,
                 trueEff=myTruthEff,
                 trueNu=1/0.025,
                 nsim=1,
                 mcmcOptions=options,
                 seed=819,
                 parallel=FALSE)



##Simulate dose-escalation procedure based on DLE and efficacy responses where DLE 
## and efficacy samples are used
## when the efficacy model is of 'EffFlexi' class
Effmodel<- EffFlexi(Eff=c(1.223, 2.513),Effdose=c(25,300),
                    sigma2=c(a=0.1,b=0.1),sigma2betaW=c(a=20,b=50),smooth="RW2",data=data)


##Specified the design 
design <- DualResponsesSamplesDesign(nextBest=mynextbest,
                                     cohortSize=mySize,
                                     startingDose=25,
                                     model=DLEmodel,
                                     Effmodel=Effmodel,
                                     data=data,
                                     stopping=myStopping,
                                     increments=myIncrements)
##specified the true DLE curve and the true expected efficacy values at all dose levels
myTruthDLE<- function(dose)
{ DLEmodel@prob(dose, phi1=-53.66584, phi2=10.50499)
}

myTruthEff<- c(-0.5478867, 0.1645417,  0.5248031,  0.7604467,  
               0.9333009  ,1.0687031,  1.1793942 , 1.2726408 , 
               1.3529598 , 1.4233411 , 1.4858613 , 1.5420182)
##The true gain curve can also be seen
d1 <- data@doseGrid
myTruthGain <- (myTruthEff)/(1+(myTruthDLE(d1)/(1-myTruthDLE(d1))))


mySim<-simulate(object=design,
                args=NULL,
                trueDLE=myTruthDLE,
                trueEff=myTruthEff,
                trueSigma2=0.025,
                trueSigma2betaW=1,
                mcmcOptions=options,
                nsim=1,
                seed=819,
                parallel=FALSE)

Simulate outcomes from a rule-based design

Description

Simulate outcomes from a rule-based design

Usage

## S4 method for signature 'RuleDesign'
simulate(
  object,
  nsim = 1L,
  seed = NULL,
  truth,
  args = NULL,
  parallel = FALSE,
  nCores = min(parallel::detectCores(), 5),
  ...
)
## S4 method for signature 'RuleDesign'
simulate(
  object,
  nsim = 1L,
  seed = NULL,
  truth,
  args = NULL,
  parallel = FALSE,
  nCores = min(parallel::detectCores(), 5),
  ...
)

Arguments

`object`	the `RuleDesign` object we want to simulate data from
`nsim`	the number of simulations (default: 1)
`seed`	see `setSeed`
`truth`	a function which takes as input a dose (vector) and returns the true probability (vector) for toxicity. Additional arguments can be supplied in `args`.
`args`	data frame with arguments for the `truth` function. The column names correspond to the argument names, the rows to the values of the arguments. The rows are appropriately recycled in the `nsim` simulations.
`parallel`	should the simulation runs be parallelized across the clusters of the computer? (not default)
`nCores`	how many cores should be used for parallel computing? Defaults to the number of cores on the machine, maximum 5.
`...`	not used

Value

an object of class GeneralSimulations

Examples


# Define the dose-grid
emptydata <- Data(doseGrid = c(5, 10, 15, 25, 35, 50, 80))

# inizialing a 3+3 design with constant cohort size of 3 and
# starting dose equal 5
myDesign <- RuleDesign(nextBest = NextBestThreePlusThree(),
                       cohortSize = CohortSizeConst(size=3L),
                       data = emptydata,
                       startingDose = 5)

model <- LogisticLogNormal(mean = c(-0.85, 1),
                           cov = matrix(c(1, -0.5, -0.5, 1), nrow = 2),
                           refDose = 50)
  
## define the true function
myTruth <- function(dose)
{
  model@prob(dose, alpha0=7, alpha1=8)
}

# Perform the simulation
##For illustration purpose only 10 simulation is produced (nsim=10). 
threeSims <- simulate(myDesign,
                      nsim=10,
                      seed=35,
                      truth=myTruth,
                      parallel=FALSE)
# Define the dose-grid
emptydata <- Data(doseGrid = c(5, 10, 15, 25, 35, 50, 80))

# inizialing a 3+3 design with constant cohort size of 3 and
# starting dose equal 5
myDesign <- RuleDesign(nextBest = NextBestThreePlusThree(),
                       cohortSize = CohortSizeConst(size=3L),
                       data = emptydata,
                       startingDose = 5)

model <- LogisticLogNormal(mean = c(-0.85, 1),
                           cov = matrix(c(1, -0.5, -0.5, 1), nrow = 2),
                           refDose = 50)
  
## define the true function
myTruth <- function(dose)
{
  model@prob(dose, alpha0=7, alpha1=8)
}

# Perform the simulation
##For illustration purpose only 10 simulation is produced (nsim=10). 
threeSims <- simulate(myDesign,
                      nsim=10,
                      seed=35,
                      truth=myTruth,
                      parallel=FALSE)

This is a methods to simulate dose escalation procedure only using the DLE responses. This is a method based on the `TDDesign` where model used are of `ModelTox` class object and no samples are involved.

Description

This is a methods to simulate dose escalation procedure only using the DLE responses. This is a method based on the TDDesign where model used are of ModelTox class object and no samples are involved.

Usage

## S4 method for signature 'TDDesign'
simulate(
  object,
  nsim = 1L,
  seed = NULL,
  truth,
  args = NULL,
  firstSeparate = FALSE,
  parallel = FALSE,
  nCores = min(parallel::detectCores(), 5),
  ...
)
## S4 method for signature 'TDDesign'
simulate(
  object,
  nsim = 1L,
  seed = NULL,
  truth,
  args = NULL,
  firstSeparate = FALSE,
  parallel = FALSE,
  nCores = min(parallel::detectCores(), 5),
  ...
)

Arguments

`object`	the `TDDesign` object we want to simulate the data from
`nsim`	the number of simulations (default :1)
`seed`	see `setSeed`
`truth`	a function which takes as input a dose (vector) and returns the true probability (vector) of the occurrence of a DLE. Additional arguments can be supplied in `args`.
`args`	data frame with arguments for the `truth` function. The column names correspond to the argument names, the rows to the values of the arguments. The rows are appropriately recycled in the `nsim` simulations. In order to produce outcomes from the posterior predictive distribution, e.g, pass an `object` that contains the data observed so far, `truth` contains the `prob` function from the model in `object`, and `args` contains posterior samples from the model.
`firstSeparate`	enroll the first patient separately from the rest of the cohort? (not default) If yes, the cohort will be closed if a DLT occurs in this patient.
`parallel`	should the simulation runs be parallelized across the clusters of the computer? (not default)
`nCores`	how many cores should be used for parallel computing? Defaults to the number of cores on the machine, maximum 5.
`...`	not used

Value

an object of class PseudoSimulations

@export @keywords methods

Examples

##Simulate dose-escalation procedure based only on DLE responses and no DLE samples are used

##The design comprises a model, the escalation rule, starting data, 
##a cohort size and a starting dose
##Define your data set first using an empty data set 
## with dose levels from 25 to 300 with increments 25
data <- Data(doseGrid=seq(25,300,25))

##The design only incorporate DLE responses and DLE samples are involved
##Specified the model of 'ModelTox' class eg 'LogisticIndepBeta' class model
model<-LogisticIndepBeta(binDLE=c(1.05,1.8),DLEweights=c(3,3),DLEdose=c(25,300),data=data)
##Then the escalation rule
tdNextBest <- NextBestTD(targetDuringTrial=0.35,
                         targetEndOfTrial=0.3)
doseRecommendation<-nextBest(tdNextBest,
                             doselimit=max(data@doseGrid),
                             model=model,
                             data=data)
##Then the starting data, an empty data set
emptydata<-Data(doseGrid=seq(25,300,25))
## The cohort size, size of 3 subjects
mySize <-CohortSizeConst(size=3)
##Deifne the increments for the dose-escalation process
##The maximum increase of 200% for doses up to the maximum of the dose specified in the doseGrid
##The maximum increase of 200% for dose above the maximum of the dose specified in the doseGrid
##This is to specified a maximum of 3-fold restriction in dose-esclation
myIncrements<-IncrementsRelative(intervals=c(min(data@doseGrid),max(data@doseGrid)), 
                                 increments=c(2,2))
##Specified the stopping rule e.g stop when the maximum sample size of 36 patients has been reached
myStopping <- StoppingMinPatients(nPatients=36)


##Specified the design(for details please refer to the 'TDDesign' example)
design <- TDDesign(model=model,
                   nextBest=tdNextBest,
                   stopping=myStopping,
                   increments=myIncrements,
                   cohortSize=mySize,
                   data=data,startingDose=25)

##Specify the truth of the DLE responses
myTruth <- function(dose)
{ model@prob(dose, phi1=-53.66584, phi2=10.50499)
}
##then plot the truth to see how the truth dose-DLE curve look like
curve(myTruth(x), from=0, to=300,ylim=c(0,1))


##For illustration purpose only 1 simulation is produced (nsim=1). 
mySim <- simulate(object=design,
                  args=NULL,
                  truth=myTruth,
                  nsim=1,
                  seed=819,
                  parallel=FALSE)
##Simulate dose-escalation procedure based only on DLE responses and no DLE samples are used

##The design comprises a model, the escalation rule, starting data, 
##a cohort size and a starting dose
##Define your data set first using an empty data set 
## with dose levels from 25 to 300 with increments 25
data <- Data(doseGrid=seq(25,300,25))

##The design only incorporate DLE responses and DLE samples are involved
##Specified the model of 'ModelTox' class eg 'LogisticIndepBeta' class model
model<-LogisticIndepBeta(binDLE=c(1.05,1.8),DLEweights=c(3,3),DLEdose=c(25,300),data=data)
##Then the escalation rule
tdNextBest <- NextBestTD(targetDuringTrial=0.35,
                         targetEndOfTrial=0.3)
doseRecommendation<-nextBest(tdNextBest,
                             doselimit=max(data@doseGrid),
                             model=model,
                             data=data)
##Then the starting data, an empty data set
emptydata<-Data(doseGrid=seq(25,300,25))
## The cohort size, size of 3 subjects
mySize <-CohortSizeConst(size=3)
##Deifne the increments for the dose-escalation process
##The maximum increase of 200% for doses up to the maximum of the dose specified in the doseGrid
##The maximum increase of 200% for dose above the maximum of the dose specified in the doseGrid
##This is to specified a maximum of 3-fold restriction in dose-esclation
myIncrements<-IncrementsRelative(intervals=c(min(data@doseGrid),max(data@doseGrid)), 
                                 increments=c(2,2))
##Specified the stopping rule e.g stop when the maximum sample size of 36 patients has been reached
myStopping <- StoppingMinPatients(nPatients=36)


##Specified the design(for details please refer to the 'TDDesign' example)
design <- TDDesign(model=model,
                   nextBest=tdNextBest,
                   stopping=myStopping,
                   increments=myIncrements,
                   cohortSize=mySize,
                   data=data,startingDose=25)

##Specify the truth of the DLE responses
myTruth <- function(dose)
{ model@prob(dose, phi1=-53.66584, phi2=10.50499)
}
##then plot the truth to see how the truth dose-DLE curve look like
curve(myTruth(x), from=0, to=300,ylim=c(0,1))


##For illustration purpose only 1 simulation is produced (nsim=1). 
mySim <- simulate(object=design,
                  args=NULL,
                  truth=myTruth,
                  nsim=1,
                  seed=819,
                  parallel=FALSE)

This is a methods to simulate dose escalation procedure only using the DLE responses. This is a method based on the `TDsamplesDesign` where model used are of `ModelTox` class object DLE samples are also used

Description

This is a methods to simulate dose escalation procedure only using the DLE responses. This is a method based on the TDsamplesDesign where model used are of ModelTox class object DLE samples are also used

Usage

## S4 method for signature 'TDsamplesDesign'
simulate(
  object,
  nsim = 1L,
  seed = NULL,
  truth,
  args = NULL,
  firstSeparate = FALSE,
  mcmcOptions = McmcOptions(),
  parallel = FALSE,
  nCores = min(parallel::detectCores(), 5),
  ...
)
## S4 method for signature 'TDsamplesDesign'
simulate(
  object,
  nsim = 1L,
  seed = NULL,
  truth,
  args = NULL,
  firstSeparate = FALSE,
  mcmcOptions = McmcOptions(),
  parallel = FALSE,
  nCores = min(parallel::detectCores(), 5),
  ...
)

Arguments

`object`	the `TDsamplesDesign` object we want to simulate the data from
`nsim`	the number of simulations (default :1)
`seed`	see `setSeed`
`truth`	a function which takes as input a dose (vector) and returns the true probability (vector) of the occurrence of a DLE. Additional arguments can be supplied in `args`.
`args`	data frame with arguments for the `truth` function. The column names correspond to the argument names, the rows to the values of the arguments. The rows are appropriately recycled in the `nsim` simulations. In order to produce outcomes from the posterior predictive distribution, e.g, pass an `object` that contains the data observed so far, `truth` contains the `prob` function from the model in `object`, and `args` contains posterior samples from the model.
`firstSeparate`	enroll the first patient separately from the rest of the cohort? (not default) If yes, the cohort will be closed if a DLT occurs in this patient.
`mcmcOptions`	object of class `McmcOptions`, giving the MCMC options for each evaluation in the trial. By default, the standard options are used
`parallel`	should the simulation runs be parallelized across the clusters of the computer? (not default)
`nCores`	how many cores should be used for parallel computing? Defaults to the number of cores on the machine, maximum 5.
`...`	not used

Value

an object of class PseudoSimulations

@export @keywords methods

Examples

##Simulate dose-escalation procedure based only on DLE responses with DLE samples involved

##The design comprises a model, the escalation rule, starting data, 
##a cohort size and a starting dose
##Define your data set first using an empty data set 
## with dose levels from 25 to 300 with increments 25
data <- Data(doseGrid=seq(25,300,25))

##The design only incorporate DLE responses and DLE samples are involved
##Specified the model of 'ModelTox' class eg 'LogisticIndepBeta' class model
model<-LogisticIndepBeta(binDLE=c(1.05,1.8),DLEweights=c(3,3),DLEdose=c(25,300),data=data)
##Then the escalation rule
tdNextBest<-NextBestTDsamples(targetDuringTrial=0.35,
                              targetEndOfTrial=0.3,
                              derive=function(TDsamples){quantile(TDsamples,probs=0.3)})


## The cohort size, size of 3 subjects
mySize <-CohortSizeConst(size=3)
##Deifne the increments for the dose-escalation process
##The maximum increase of 200% for doses up to the maximum of the dose specified in the doseGrid
##The maximum increase of 200% for dose above the maximum of the dose specified in the doseGrid
##This is to specified a maximum of 3-fold restriction in dose-esclation
myIncrements<-IncrementsRelative(intervals=c(min(data@doseGrid),max(data@doseGrid)), 
                                 increments=c(2,2))
##Specified the stopping rule e.g stop when the maximum sample size of 36 patients has been reached
myStopping <- StoppingMinPatients(nPatients=36)

##Specified the design(for details please refer to the 'TDsamplesDesign' example)
design <- TDsamplesDesign(model=model,
                          nextBest=tdNextBest,
                          stopping=myStopping,
                          increments=myIncrements,
                          cohortSize=mySize,
                          data=data,startingDose=25)

##Specify the truth of the DLE responses
myTruth <- function(dose)
{ model@prob(dose, phi1=-53.66584, phi2=10.50499)
}
##then plot the truth to see how the truth dose-DLE curve look like
curve(myTruth(x), from=0, to=300,ylim=c(0,1))

## Then specified the simulations and generate the trial
##options for MCMC
options<-McmcOptions(burnin=100,step=2,samples=200)
##The simulations
##For illustration purpose only 1 simulation is produced (nsim=1). 
mySim <-  simulate(object=design,
                   args=NULL,
                   truth=myTruth,
                   nsim=1,
                   seed=819,
                   mcmcOptions=options,
                   parallel=FALSE)
##Simulate dose-escalation procedure based only on DLE responses with DLE samples involved

##The design comprises a model, the escalation rule, starting data, 
##a cohort size and a starting dose
##Define your data set first using an empty data set 
## with dose levels from 25 to 300 with increments 25
data <- Data(doseGrid=seq(25,300,25))

##The design only incorporate DLE responses and DLE samples are involved
##Specified the model of 'ModelTox' class eg 'LogisticIndepBeta' class model
model<-LogisticIndepBeta(binDLE=c(1.05,1.8),DLEweights=c(3,3),DLEdose=c(25,300),data=data)
##Then the escalation rule
tdNextBest<-NextBestTDsamples(targetDuringTrial=0.35,
                              targetEndOfTrial=0.3,
                              derive=function(TDsamples){quantile(TDsamples,probs=0.3)})


## The cohort size, size of 3 subjects
mySize <-CohortSizeConst(size=3)
##Deifne the increments for the dose-escalation process
##The maximum increase of 200% for doses up to the maximum of the dose specified in the doseGrid
##The maximum increase of 200% for dose above the maximum of the dose specified in the doseGrid
##This is to specified a maximum of 3-fold restriction in dose-esclation
myIncrements<-IncrementsRelative(intervals=c(min(data@doseGrid),max(data@doseGrid)), 
                                 increments=c(2,2))
##Specified the stopping rule e.g stop when the maximum sample size of 36 patients has been reached
myStopping <- StoppingMinPatients(nPatients=36)

##Specified the design(for details please refer to the 'TDsamplesDesign' example)
design <- TDsamplesDesign(model=model,
                          nextBest=tdNextBest,
                          stopping=myStopping,
                          increments=myIncrements,
                          cohortSize=mySize,
                          data=data,startingDose=25)

##Specify the truth of the DLE responses
myTruth <- function(dose)
{ model@prob(dose, phi1=-53.66584, phi2=10.50499)
}
##then plot the truth to see how the truth dose-DLE curve look like
curve(myTruth(x), from=0, to=300,ylim=c(0,1))

## Then specified the simulations and generate the trial
##options for MCMC
options<-McmcOptions(burnin=100,step=2,samples=200)
##The simulations
##For illustration purpose only 1 simulation is produced (nsim=1). 
mySim <-  simulate(object=design,
                   args=NULL,
                   truth=myTruth,
                   nsim=1,
                   seed=819,
                   mcmcOptions=options,
                   parallel=FALSE)

Initialization function for the "Simulations" class

Description

Initialization function for the "Simulations" class

Usage

Simulations(fit, stopReasons, ...)
Simulations(fit, stopReasons, ...)

Arguments

`fit`	see `Simulations`
`stopReasons`	see `Simulations`
`...`	additional parameters from `GeneralSimulations`

Value

the Simulations object

Class for the simulations output from model based designs

Description

This class captures the trial simulations from model based designs. Additional slots fit and stopReasons compared to the general class GeneralSimulations.

Slots

fit: list with the final fits
stopReasons: list of stopping reasons for each simulation run

Class for the summary of model-based simulations output

Description

In addition to the slots in the parent class GeneralSimulationsSummary, it contains two slots with model fit information.

Details

Note that objects should not be created by users, therefore no initialization function is provided for this class.

Slots

fitAtDoseMostSelected: fitted toxicity rate at dose most often selected
meanFit: list with the average, lower (2.5 quantiles of the mean fitted toxicity at each dose level

Determine the size of the next cohort

Description

This function determines the size of the next cohort.

Usage

size(cohortSize, dose, data, ...)

## S4 method for signature 'CohortSizeRange,ANY,Data'
size(cohortSize, dose, data, ...)

## S4 method for signature 'CohortSizeDLT,ANY,Data'
size(cohortSize, dose, data, ...)

## S4 method for signature 'CohortSizeMax,ANY,Data'
size(cohortSize, dose, data, ...)

## S4 method for signature 'CohortSizeMin,ANY,Data'
size(cohortSize, dose, data, ...)

## S4 method for signature 'CohortSizeConst,ANY,Data'
size(cohortSize, dose, data, ...)

## S4 method for signature 'CohortSizeParts,ANY,DataParts'
size(cohortSize, dose, data, ...)
size(cohortSize, dose, data, ...)

## S4 method for signature 'CohortSizeRange,ANY,Data'
size(cohortSize, dose, data, ...)

## S4 method for signature 'CohortSizeDLT,ANY,Data'
size(cohortSize, dose, data, ...)

## S4 method for signature 'CohortSizeMax,ANY,Data'
size(cohortSize, dose, data, ...)

## S4 method for signature 'CohortSizeMin,ANY,Data'
size(cohortSize, dose, data, ...)

## S4 method for signature 'CohortSizeConst,ANY,Data'
size(cohortSize, dose, data, ...)

## S4 method for signature 'CohortSizeParts,ANY,DataParts'
size(cohortSize, dose, data, ...)

Arguments

`cohortSize`	The rule, an object of class `CohortSize`
`dose`	the next dose
`data`	The data input, an object of class `Data`
`...`	additional arguments

Value

the size as integer value

Functions

size(cohortSize = CohortSizeRange, dose = ANY, data = Data): Determine the cohort size based on the range into which the next dose falls into
size(cohortSize = CohortSizeDLT, dose = ANY, data = Data): Determine the cohort size based on the number of DLTs so far
size(cohortSize = CohortSizeMax, dose = ANY, data = Data): Size based on maximum of multiple cohort size rules
size(cohortSize = CohortSizeMin, dose = ANY, data = Data): Size based on minimum of multiple cohort size rules
size(cohortSize = CohortSizeConst, dose = ANY, data = Data): Constant cohort size
size(cohortSize = CohortSizeParts, dose = ANY, data = DataParts): Cohort size based on the parts

Examples


# Create the data
data <- Data(x=c(0.1, 0.5, 1.5, 3, 6, 10, 10, 10),
             y=c(0, 0, 0, 0, 0, 0, 1, 0),
             cohort=c(0, 1, 2, 3, 4, 5, 5, 5),
             doseGrid=
               c(0.1, 0.5, 1.5, 3, 6,
                 seq(from=10, to=80, by=2)))

# Initialize the CRM model used to model the data
model <- LogisticLogNormal(mean=c(-0.85, 1),
                           cov=
                             matrix(c(1, -0.5, -0.5, 1),
                                    nrow=2),
                           refDose=56)

# Set-up some MCMC parameters and generate samples from the posterior
options <- McmcOptions(burnin=100,
                       step=2,
                       samples=2000)
set.seed(94)
samples <- mcmc(data, model, options)

# Define the rule for dose increments and calculate the maximum dose allowed
myIncrements <- IncrementsRelative(intervals=c(0, 20),
                                   increments=c(1, 0.33))
nextMaxDose <- maxDose(myIncrements,
                       data=data)

# Define the rule which will be used to select the next best dose
# based on the class 'NextBestNCRM'
myNextBest <- NextBestNCRM(target=c(0.2, 0.35),
                           overdose=c(0.35, 1),
                           maxOverdoseProb=0.25)

# Calculate the next best dose
doseRecommendation <- nextBest(myNextBest,
                               doselimit=nextMaxDose,
                               samples=samples, model=model, data=data)

# Rule for the cohort size: 
#   - having cohort of size 1 for doses <10
#   - and having cohort of size 3 for doses >=10
mySize <- CohortSizeRange(intervals=c(0, 10),
                          cohortSize=c(1, 3))

# Determine the cohort size for the next cohort
size(mySize, dose=doseRecommendation$value, data = data)


# Create the data
data <- Data(x=c(0.1, 0.5, 1.5, 3, 6, 10, 10, 10),
             y=c(0, 0, 0, 0, 0, 0, 1, 0),
             cohort=c(0, 1, 2, 3, 4, 5, 5, 5),
             doseGrid=
               c(0.1, 0.5, 1.5, 3, 6,
                 seq(from=10, to=80, by=2)))

# Initialize the CRM model used to model the data
model <- LogisticLogNormal(mean=c(-0.85, 1),
                           cov=
                             matrix(c(1, -0.5, -0.5, 1),
                                    nrow=2),
                           refDose=56)

# Set-up some MCMC parameters and generate samples from the posterior
options <- McmcOptions(burnin=100,
                       step=2,
                       samples=2000)
set.seed(94)
samples <- mcmc(data, model, options)

# Define the rule for dose increments and calculate the maximum dose allowed
myIncrements <- IncrementsRelative(intervals=c(0, 20),
                                   increments=c(1, 0.33))
nextMaxDose <- maxDose(myIncrements,
                       data=data)

# Define the rule which will be used to select the next best dose
# based on the class 'NextBestNCRM'
myNextBest <- NextBestNCRM(target=c(0.2, 0.35),
                           overdose=c(0.35, 1),
                           maxOverdoseProb=0.25)

# Calculate the next best dose
doseRecommendation <- nextBest(myNextBest,
                               doselimit=nextMaxDose,
                               samples=samples, model=model, data=data)

# Rule for the cohort size: 
#   - having cohort of size 1 if no DLTs were yet observed
#   - and having cohort of size 3 if at least 1 DLT was already observed
mySize <- CohortSizeDLT(DLTintervals = c(0, 1),
                        cohortSize = c(1, 3))

# Determine the cohort size for the next cohort
size(mySize, dose=doseRecommendation$value, data = data)


# Create the data
data <- Data(x=c(0.1, 0.5, 1.5, 3, 6, 10, 10, 10),
             y=c(0, 0, 0, 0, 0, 0, 1, 0),
             cohort=c(0, 1, 2, 3, 4, 5, 5, 5),
             doseGrid=
               c(0.1, 0.5, 1.5, 3, 6,
                 seq(from=10, to=80, by=2)))

# Initialize the CRM model used to model the data
model <- LogisticLogNormal(mean=c(-0.85, 1),
                           cov=
                             matrix(c(1, -0.5, -0.5, 1),
                                    nrow=2),
                           refDose=56)

# Set-up some MCMC parameters and generate samples from the posterior
options <- McmcOptions(burnin=100,
                       step=2,
                       samples=2000)
set.seed(94)
samples <- mcmc(data, model, options)

# Define the rule for dose increments and calculate the maximum dose allowed
myIncrements <- IncrementsRelative(intervals=c(0, 20),
                                   increments=c(1, 0.33))
nextMaxDose <- maxDose(myIncrements,
                       data=data)

# Define the rule which will be used to select the next best dose
# based on the class 'NextBestNCRM'
myNextBest <- NextBestNCRM(target=c(0.2, 0.35),
                           overdose=c(0.35, 1),
                           maxOverdoseProb=0.25)

# Calculate the next best dose
doseRecommendation <- nextBest(myNextBest,
                               doselimit=nextMaxDose,
                               samples=samples, model=model, data=data)

# Rule for having cohort of size 1 for doses <30
#      and having cohort of size 3 for doses >=30
mySize1 <- CohortSizeRange(intervals = c(0, 10),
                           cohortSize = c(1, 3))

# Rule for having cohort of size 1 until no DLT were observed
#      and having cohort of size 3 as soon as 1 DLT is observed
mySize2 <- CohortSizeDLT(DLTintervals=c(0, 1),
                         cohortSize=c(1, 3))

# Combining the two rules for cohort size by taking the maximum of the sample sizes
# of the single rules
mySize <- maxSize(mySize1, mySize2)

# Determine the cohort size for the next cohort
size(mySize, dose=doseRecommendation$value, data = data)




# Create the data
data <- Data(x=c(0.1, 0.5, 1.5, 3, 6, 10, 10, 10),
             y=c(0, 0, 0, 0, 0, 0, 1, 0),
             cohort=c(0, 1, 2, 3, 4, 5, 5, 5),
             doseGrid=
               c(0.1, 0.5, 1.5, 3, 6,
                 seq(from=10, to=80, by=2)))

# Initialize the CRM model used to model the data
model <- LogisticLogNormal(mean=c(-0.85, 1),
                           cov=
                             matrix(c(1, -0.5, -0.5, 1),
                                    nrow=2),
                           refDose=56)

# Set-up some MCMC parameters and generate samples from the posterior
options <- McmcOptions(burnin=100,
                       step=2,
                       samples=2000)
set.seed(94)
samples <- mcmc(data, model, options)

# Define the rule for dose increments and calculate the maximum dose allowed
myIncrements <- IncrementsRelative(intervals=c(0, 20),
                                   increments=c(1, 0.33))
nextMaxDose <- maxDose(myIncrements,
                       data=data)

# Define the rule which will be used to select the next best dose
# based on the class 'NextBestNCRM'
myNextBest <- NextBestNCRM(target=c(0.2, 0.35),
                           overdose=c(0.35, 1),
                           maxOverdoseProb=0.25)

# Calculate the next best dose
doseRecommendation <- nextBest(myNextBest,
                               doselimit=nextMaxDose,
                               samples=samples, model=model, data=data)

# Rule for having cohort of size 1 for doses <30
#      and having cohort of size 3 for doses >=30
mySize1 <- CohortSizeRange(intervals = c(0, 30),
                           cohortSize = c(1, 3))

# Rule for having cohort of size 1 until no DLT were observed
#      and having cohort of size 3 as soon as 1 DLT is observed
mySize2 <- CohortSizeDLT(DLTintervals=c(0, 1),
                         cohortSize=c(1, 3))

# Combining the two rules for cohort size by taking the minimum of the sample sizes
# of the single rules
mySize <- minSize(mySize1, mySize2)

# Determine the cohort size for the next cohort
size(mySize, dose=doseRecommendation$value, data = data)




# Create the data
data <- Data(x=c(0.1, 0.5, 1.5, 3, 6, 10, 10, 10),
             y=c(0, 0, 0, 0, 0, 0, 1, 0),
             cohort=c(0, 1, 2, 3, 4, 5, 5, 5),
             doseGrid=
               c(0.1, 0.5, 1.5, 3, 6,
                 seq(from=10, to=80, by=2)))

# Initialize the CRM model used to model the data
model <- LogisticLogNormal(mean=c(-0.85, 1),
                           cov=
                             matrix(c(1, -0.5, -0.5, 1),
                                    nrow=2),
                           refDose=56)

# Set-up some MCMC parameters and generate samples from the posterior
options <- McmcOptions(burnin=100,
                       step=2,
                       samples=2000)
set.seed(94)
samples <- mcmc(data, model, options)

# Define the rule for dose increments and calculate the maximum dose allowed
myIncrements <- IncrementsRelative(intervals=c(0, 20),
                                   increments=c(1, 0.33))
nextMaxDose <- maxDose(myIncrements,
                       data=data)

# Define the rule which will be used to select the next best dose
# based on the class 'NextBestNCRM'
myNextBest <- NextBestNCRM(target=c(0.2, 0.35),
                           overdose=c(0.35, 1),
                           maxOverdoseProb=0.25)

# Calculate the next best dose
doseRecommendation <- nextBest(myNextBest,
                               doselimit=nextMaxDose,
                               samples=samples, model=model, data=data)

# Rule for having cohorts with constant cohort size of 3
mySize <- CohortSizeConst(size=3)

# Determine the cohort size for the next cohort
size(mySize, dose=doseRecommendation$value, data = data)




# create an object of class 'DataParts'
data <- DataParts(x=c(0.1,0.5,1.5),
                  y=c(0,0,0),
                  doseGrid=c(0.1,0.5,1.5,3,6,
                             seq(from=10,to=80,by=2)),
                  part=c(1L,1L,1L),
                  nextPart=1L,
                  part1Ladder=c(0.1,0.5,1.5,3,6,10))

# Initialize the CRM model used to model the data
model <- LogisticLogNormal(mean=c(-0.85, 1),
                           cov=
                             matrix(c(1, -0.5, -0.5, 1),
                                    nrow=2),
                           refDose=56)

# Set-up some MCMC parameters and generate samples from the posterior
options <- McmcOptions(burnin=100,
                       step=2,
                       samples=2000)
set.seed(94)
samples <- mcmc(data, model, options)

myIncrements <- IncrementsRelativeParts(dltStart=0,
                                        cleanStart=1)
nextMaxDose <- maxDose(myIncrements,
                       data=data)

# Define the rule which will be used to select the next best dose
# based on the class 'NextBestNCRM'
myNextBest <- NextBestNCRM(target=c(0.2, 0.35),
                           overdose=c(0.35, 1),
                           maxOverdoseProb=0.25)

# Calculate the next best dose
doseRecommendation <- nextBest(myNextBest,
                               doselimit=nextMaxDose,
                               samples=samples, 
                               model=model, 
                               data=data)

# Rule for the cohort size: 
mySize <- CohortSizeParts(sizes=c(1,3))

# Determine the cohort size for the next cohort
size(mySize, dose=doseRecommendation$value, data = data)

# Create the data
data <- Data(x=c(0.1, 0.5, 1.5, 3, 6, 10, 10, 10),
             y=c(0, 0, 0, 0, 0, 0, 1, 0),
             cohort=c(0, 1, 2, 3, 4, 5, 5, 5),
             doseGrid=
               c(0.1, 0.5, 1.5, 3, 6,
                 seq(from=10, to=80, by=2)))

# Initialize the CRM model used to model the data
model <- LogisticLogNormal(mean=c(-0.85, 1),
                           cov=
                             matrix(c(1, -0.5, -0.5, 1),
                                    nrow=2),
                           refDose=56)

# Set-up some MCMC parameters and generate samples from the posterior
options <- McmcOptions(burnin=100,
                       step=2,
                       samples=2000)
set.seed(94)
samples <- mcmc(data, model, options)

# Define the rule for dose increments and calculate the maximum dose allowed
myIncrements <- IncrementsRelative(intervals=c(0, 20),
                                   increments=c(1, 0.33))
nextMaxDose <- maxDose(myIncrements,
                       data=data)

# Define the rule which will be used to select the next best dose
# based on the class 'NextBestNCRM'
myNextBest <- NextBestNCRM(target=c(0.2, 0.35),
                           overdose=c(0.35, 1),
                           maxOverdoseProb=0.25)

# Calculate the next best dose
doseRecommendation <- nextBest(myNextBest,
                               doselimit=nextMaxDose,
                               samples=samples, model=model, data=data)

# Rule for the cohort size: 
#   - having cohort of size 1 for doses <10
#   - and having cohort of size 3 for doses >=10
mySize <- CohortSizeRange(intervals=c(0, 10),
                          cohortSize=c(1, 3))

# Determine the cohort size for the next cohort
size(mySize, dose=doseRecommendation$value, data = data)


# Create the data
data <- Data(x=c(0.1, 0.5, 1.5, 3, 6, 10, 10, 10),
             y=c(0, 0, 0, 0, 0, 0, 1, 0),
             cohort=c(0, 1, 2, 3, 4, 5, 5, 5),
             doseGrid=
               c(0.1, 0.5, 1.5, 3, 6,
                 seq(from=10, to=80, by=2)))

# Initialize the CRM model used to model the data
model <- LogisticLogNormal(mean=c(-0.85, 1),
                           cov=
                             matrix(c(1, -0.5, -0.5, 1),
                                    nrow=2),
                           refDose=56)

# Set-up some MCMC parameters and generate samples from the posterior
options <- McmcOptions(burnin=100,
                       step=2,
                       samples=2000)
set.seed(94)
samples <- mcmc(data, model, options)

# Define the rule for dose increments and calculate the maximum dose allowed
myIncrements <- IncrementsRelative(intervals=c(0, 20),
                                   increments=c(1, 0.33))
nextMaxDose <- maxDose(myIncrements,
                       data=data)

# Define the rule which will be used to select the next best dose
# based on the class 'NextBestNCRM'
myNextBest <- NextBestNCRM(target=c(0.2, 0.35),
                           overdose=c(0.35, 1),
                           maxOverdoseProb=0.25)

# Calculate the next best dose
doseRecommendation <- nextBest(myNextBest,
                               doselimit=nextMaxDose,
                               samples=samples, model=model, data=data)

# Rule for the cohort size: 
#   - having cohort of size 1 if no DLTs were yet observed
#   - and having cohort of size 3 if at least 1 DLT was already observed
mySize <- CohortSizeDLT(DLTintervals = c(0, 1),
                        cohortSize = c(1, 3))

# Determine the cohort size for the next cohort
size(mySize, dose=doseRecommendation$value, data = data)


# Create the data
data <- Data(x=c(0.1, 0.5, 1.5, 3, 6, 10, 10, 10),
             y=c(0, 0, 0, 0, 0, 0, 1, 0),
             cohort=c(0, 1, 2, 3, 4, 5, 5, 5),
             doseGrid=
               c(0.1, 0.5, 1.5, 3, 6,
                 seq(from=10, to=80, by=2)))

# Initialize the CRM model used to model the data
model <- LogisticLogNormal(mean=c(-0.85, 1),
                           cov=
                             matrix(c(1, -0.5, -0.5, 1),
                                    nrow=2),
                           refDose=56)

# Set-up some MCMC parameters and generate samples from the posterior
options <- McmcOptions(burnin=100,
                       step=2,
                       samples=2000)
set.seed(94)
samples <- mcmc(data, model, options)

# Define the rule for dose increments and calculate the maximum dose allowed
myIncrements <- IncrementsRelative(intervals=c(0, 20),
                                   increments=c(1, 0.33))
nextMaxDose <- maxDose(myIncrements,
                       data=data)

# Define the rule which will be used to select the next best dose
# based on the class 'NextBestNCRM'
myNextBest <- NextBestNCRM(target=c(0.2, 0.35),
                           overdose=c(0.35, 1),
                           maxOverdoseProb=0.25)

# Calculate the next best dose
doseRecommendation <- nextBest(myNextBest,
                               doselimit=nextMaxDose,
                               samples=samples, model=model, data=data)

# Rule for having cohort of size 1 for doses <30
#      and having cohort of size 3 for doses >=30
mySize1 <- CohortSizeRange(intervals = c(0, 10),
                           cohortSize = c(1, 3))

# Rule for having cohort of size 1 until no DLT were observed
#      and having cohort of size 3 as soon as 1 DLT is observed
mySize2 <- CohortSizeDLT(DLTintervals=c(0, 1),
                         cohortSize=c(1, 3))

# Combining the two rules for cohort size by taking the maximum of the sample sizes
# of the single rules
mySize <- maxSize(mySize1, mySize2)

# Determine the cohort size for the next cohort
size(mySize, dose=doseRecommendation$value, data = data)




# Create the data
data <- Data(x=c(0.1, 0.5, 1.5, 3, 6, 10, 10, 10),
             y=c(0, 0, 0, 0, 0, 0, 1, 0),
             cohort=c(0, 1, 2, 3, 4, 5, 5, 5),
             doseGrid=
               c(0.1, 0.5, 1.5, 3, 6,
                 seq(from=10, to=80, by=2)))

# Initialize the CRM model used to model the data
model <- LogisticLogNormal(mean=c(-0.85, 1),
                           cov=
                             matrix(c(1, -0.5, -0.5, 1),
                                    nrow=2),
                           refDose=56)

# Set-up some MCMC parameters and generate samples from the posterior
options <- McmcOptions(burnin=100,
                       step=2,
                       samples=2000)
set.seed(94)
samples <- mcmc(data, model, options)

# Define the rule for dose increments and calculate the maximum dose allowed
myIncrements <- IncrementsRelative(intervals=c(0, 20),
                                   increments=c(1, 0.33))
nextMaxDose <- maxDose(myIncrements,
                       data=data)

# Define the rule which will be used to select the next best dose
# based on the class 'NextBestNCRM'
myNextBest <- NextBestNCRM(target=c(0.2, 0.35),
                           overdose=c(0.35, 1),
                           maxOverdoseProb=0.25)

# Calculate the next best dose
doseRecommendation <- nextBest(myNextBest,
                               doselimit=nextMaxDose,
                               samples=samples, model=model, data=data)

# Rule for having cohort of size 1 for doses <30
#      and having cohort of size 3 for doses >=30
mySize1 <- CohortSizeRange(intervals = c(0, 30),
                           cohortSize = c(1, 3))

# Rule for having cohort of size 1 until no DLT were observed
#      and having cohort of size 3 as soon as 1 DLT is observed
mySize2 <- CohortSizeDLT(DLTintervals=c(0, 1),
                         cohortSize=c(1, 3))

# Combining the two rules for cohort size by taking the minimum of the sample sizes
# of the single rules
mySize <- minSize(mySize1, mySize2)

# Determine the cohort size for the next cohort
size(mySize, dose=doseRecommendation$value, data = data)




# Create the data
data <- Data(x=c(0.1, 0.5, 1.5, 3, 6, 10, 10, 10),
             y=c(0, 0, 0, 0, 0, 0, 1, 0),
             cohort=c(0, 1, 2, 3, 4, 5, 5, 5),
             doseGrid=
               c(0.1, 0.5, 1.5, 3, 6,
                 seq(from=10, to=80, by=2)))

# Initialize the CRM model used to model the data
model <- LogisticLogNormal(mean=c(-0.85, 1),
                           cov=
                             matrix(c(1, -0.5, -0.5, 1),
                                    nrow=2),
                           refDose=56)

# Set-up some MCMC parameters and generate samples from the posterior
options <- McmcOptions(burnin=100,
                       step=2,
                       samples=2000)
set.seed(94)
samples <- mcmc(data, model, options)

# Define the rule for dose increments and calculate the maximum dose allowed
myIncrements <- IncrementsRelative(intervals=c(0, 20),
                                   increments=c(1, 0.33))
nextMaxDose <- maxDose(myIncrements,
                       data=data)

# Define the rule which will be used to select the next best dose
# based on the class 'NextBestNCRM'
myNextBest <- NextBestNCRM(target=c(0.2, 0.35),
                           overdose=c(0.35, 1),
                           maxOverdoseProb=0.25)

# Calculate the next best dose
doseRecommendation <- nextBest(myNextBest,
                               doselimit=nextMaxDose,
                               samples=samples, model=model, data=data)

# Rule for having cohorts with constant cohort size of 3
mySize <- CohortSizeConst(size=3)

# Determine the cohort size for the next cohort
size(mySize, dose=doseRecommendation$value, data = data)




# create an object of class 'DataParts'
data <- DataParts(x=c(0.1,0.5,1.5),
                  y=c(0,0,0),
                  doseGrid=c(0.1,0.5,1.5,3,6,
                             seq(from=10,to=80,by=2)),
                  part=c(1L,1L,1L),
                  nextPart=1L,
                  part1Ladder=c(0.1,0.5,1.5,3,6,10))

# Initialize the CRM model used to model the data
model <- LogisticLogNormal(mean=c(-0.85, 1),
                           cov=
                             matrix(c(1, -0.5, -0.5, 1),
                                    nrow=2),
                           refDose=56)

# Set-up some MCMC parameters and generate samples from the posterior
options <- McmcOptions(burnin=100,
                       step=2,
                       samples=2000)
set.seed(94)
samples <- mcmc(data, model, options)

myIncrements <- IncrementsRelativeParts(dltStart=0,
                                        cleanStart=1)
nextMaxDose <- maxDose(myIncrements,
                       data=data)

# Define the rule which will be used to select the next best dose
# based on the class 'NextBestNCRM'
myNextBest <- NextBestNCRM(target=c(0.2, 0.35),
                           overdose=c(0.35, 1),
                           maxOverdoseProb=0.25)

# Calculate the next best dose
doseRecommendation <- nextBest(myNextBest,
                               doselimit=nextMaxDose,
                               samples=samples, 
                               model=model, 
                               data=data)

# Rule for the cohort size: 
mySize <- CohortSizeParts(sizes=c(1,3))

# Determine the cohort size for the next cohort
size(mySize, dose=doseRecommendation$value, data = data)

The virtual class for stopping rules

Description

The virtual class for stopping rules

Initialization function for "StoppingAll"

Description

Initialization function for "StoppingAll"

Usage

StoppingAll(stopList)
StoppingAll(stopList)

Arguments

stopList

see StoppingAll

Value

the StoppingAll object

Stop based on fullfillment of all multiple stopping rules

Description

This class can be used to combine multiple stopping rules with an AND operator.

Details

stopList contains all stopping rules, which are again objects of class Stopping. All stopping rules must be fulfilled in order that the result of this rule is to stop.

Slots

stopList: list of stopping rules

Examples


# Define some stopping rules
myStopping1 <- StoppingMinCohorts(nCohorts=3)
myStopping2 <- StoppingTargetProb(target=c(0.2, 0.35),
                                  prob=0.5)
myStopping3 <- StoppingMinPatients(nPatients=20)

# Create a list of stopping rules (of class 'StoppingAll') which would then be
# summarized by the 'all' function, meaning that the study would be stopped only if
# 'all' the single stopping rules are TRUE
mystopping <- StoppingAll(stopList=c(myStopping1,myStopping2,myStopping3))


# Define some stopping rules
myStopping1 <- StoppingMinCohorts(nCohorts=3)
myStopping2 <- StoppingTargetProb(target=c(0.2, 0.35),
                                  prob=0.5)
myStopping3 <- StoppingMinPatients(nPatients=20)

# Create a list of stopping rules (of class 'StoppingAll') which would then be
# summarized by the 'all' function, meaning that the study would be stopped only if
# 'all' the single stopping rules are TRUE
mystopping <- StoppingAll(stopList=c(myStopping1,myStopping2,myStopping3))

Initialization function for "StoppingAny"

Description

Initialization function for "StoppingAny"

Usage

StoppingAny(stopList)
StoppingAny(stopList)

Arguments

stopList

see StoppingAny

Value

the StoppingAny object

Stop based on fullfillment of any stopping rule

Description

This class can be used to combine multiple stopping rules with an OR operator.

Details

stopList contains all stopping rules, which are again objects of class Stopping. Any of these rules must be fulfilled in order that the result of this rule is to stop.

Slots

stopList: list of stopping rules

Examples


# Define some stopping rules
myStopping1 <- StoppingMinCohorts(nCohorts=3)
myStopping2 <- StoppingTargetProb(target=c(0.2, 0.35),
                                  prob=0.5)
myStopping3 <- StoppingMinPatients(nPatients=20)

# Create a list of stopping rules (of class 'StoppingAny') which will then be
# summarized with the 'any' function, meaning that the study would be stopped if
# 'any' of the single stopping rules is TRUE.
mystopping <- StoppingAny(stopList=c(myStopping1,myStopping2,myStopping3))


# Define some stopping rules
myStopping1 <- StoppingMinCohorts(nCohorts=3)
myStopping2 <- StoppingTargetProb(target=c(0.2, 0.35),
                                  prob=0.5)
myStopping3 <- StoppingMinPatients(nPatients=20)

# Create a list of stopping rules (of class 'StoppingAny') which will then be
# summarized with the 'any' function, meaning that the study would be stopped if
# 'any' of the single stopping rules is TRUE.
mystopping <- StoppingAny(stopList=c(myStopping1,myStopping2,myStopping3))

Initialization function for "StoppingCohortsNearDose"

Description

Initialization function for "StoppingCohortsNearDose"

Usage

StoppingCohortsNearDose(nCohorts, percentage)
StoppingCohortsNearDose(nCohorts, percentage)

Arguments

`nCohorts`	see `StoppingCohortsNearDose`
`percentage`	see `StoppingCohortsNearDose`

Value

the StoppingCohortsNearDose object

Stop based on number of cohorts near to next best dose

Description

Stop based on number of cohorts near to next best dose

Slots

nCohorts: number of required cohorts
percentage: percentage (between 0 and 100) within the next best dose the cohorts must lie

Examples


# As example, here is the rule for: 
#   stopping the study if at least 3 cohorts were dosed at a dose within (1 +/- 0.2) 
#   of the next best dose

myStopping <- StoppingCohortsNearDose(nCohorts = 3,
                                      percentage = 0.2)

# As example, here is the rule for: 
#   stopping the study if at least 3 cohorts were dosed at a dose within (1 +/- 0.2) 
#   of the next best dose

myStopping <- StoppingCohortsNearDose(nCohorts = 3,
                                      percentage = 0.2)

Initialization function for "StoppingGstarCIRatio"

Description

Initialization function for "StoppingGstarCIRatio"

Usage

StoppingGstarCIRatio(targetRatio, targetEndOfTrial)
StoppingGstarCIRatio(targetRatio, targetEndOfTrial)

Arguments

`targetRatio`	please refer to `StoppingGstarCIRatio` class object
`targetEndOfTrial`	please refer to `StoppingGstarCIRatio` class object

Value

the StoppingGstarCIRatio class object

Stop based on a target ratio, the ratio of the upper to the lower 95% credibility interval of the estimate of the minimum of the dose which gives the maximum gain (Gstar) and the TD end of trial, the dose with probability of DLE equals to the target probability of DLE used at the end of a trial.

Description

Stop based on a target ratio, the ratio of the upper to the lower 95% credibility interval of the estimate of the minimum of the dose which gives the maximum gain (Gstar) and the TD end of trial, the dose with probability of DLE equals to the target probability of DLE used at the end of a trial.

Slots

targetRatio: the target ratio of the upper to the lower of the 95% credibility interval of the estimate that required to stop a trial
targetEndOfTrial: the target probability of DLE to be used at the end of a trial

Examples

##Define the target stopping ratio of 5 and 
##the target probability of DLE to be used at the end of a trial
##This is a ratio of the upper to the lower 95% credibility interval of the estimates
myStopping <- StoppingGstarCIRatio(targetRatio=5,
                                   targetEndOfTrial=0.3)
##Define the target stopping ratio of 5 and 
##the target probability of DLE to be used at the end of a trial
##This is a ratio of the upper to the lower 95% credibility interval of the estimates
myStopping <- StoppingGstarCIRatio(targetRatio=5,
                                   targetEndOfTrial=0.3)

Initialization function for "StoppingHighestDose"

Description

Initialization function for "StoppingHighestDose"

Usage

StoppingHighestDose()
StoppingHighestDose()

Value

the StoppingHighestDose object

Stop when the highest dose is reached

Description

Stop when the highest dose is reached

Examples

## for example this can be used in the following way:
## we would like to stop if:
## - next proposed dose is highest dose
## - there are already at least 3 patients on that dose
## - we are sure that this dose is safe, e.g. the
## probability to be in the interval (0%, 20%) of DLT
## rate is above 50%.
## This would be implemented as the following combination:

stopHigh <-
  StoppingHighestDose() &
  StoppingPatientsNearDose(nPatients=3, percentage=0) &
  StoppingTargetProb(target=c(0, 0.2),
                     prob=0.5)

## of course this rule would then need to be combined
## with the other standard rules for when to stop
## when the MTD is found based on being near
## e.g. a 30% DLT probability or having reached maximal sample
## size, in the manner of:
## stopRule <- stopHigh | stopLow | stopSamplesize
## for example this can be used in the following way:
## we would like to stop if:
## - next proposed dose is highest dose
## - there are already at least 3 patients on that dose
## - we are sure that this dose is safe, e.g. the
## probability to be in the interval (0%, 20%) of DLT
## rate is above 50%.
## This would be implemented as the following combination:

stopHigh <-
  StoppingHighestDose() &
  StoppingPatientsNearDose(nPatients=3, percentage=0) &
  StoppingTargetProb(target=c(0, 0.2),
                     prob=0.5)

## of course this rule would then need to be combined
## with the other standard rules for when to stop
## when the MTD is found based on being near
## e.g. a 30% DLT probability or having reached maximal sample
## size, in the manner of:
## stopRule <- stopHigh | stopLow | stopSamplesize

Initialization function for "StoppingList"

Description

Initialization function for "StoppingList"

Usage

StoppingList(stopList, summary)
StoppingList(stopList, summary)

Arguments

`stopList`	see `StoppingList`
`summary`	see `StoppingList`

Value

the StoppingList object

Stop based on multiple stopping rules

Description

This class can be used to combine multiple stopping rules.

Details

stopList contains all stopping rules, which are again objects of class Stopping, and the summary is a function taking a logical vector of the size of stopList and returning a single logical value. For example, if the function all is given as summary function, then this means that all stopping rules must be fulfilled in order that the result of this rule is to stop.

Slots

stopList: list of stopping rules
summary: the summary function to combine the results of the stopping rules into a single result

Examples


# Define some stopping rules
myStopping1 <- StoppingMinCohorts(nCohorts=3)
myStopping2 <- StoppingTargetProb(target=c(0.2, 0.35),
                                  prob=0.5)
myStopping3 <- StoppingMinPatients(nPatients=20)

# Create a list of stopping rules (of class 'StoppingList') which will then be
# summarized (in this specific example) with the 'any' function, meaning that the study 
# would be stopped if 'any' of the single stopping rules is TRUE.
mystopping <- StoppingList(stopList=c(myStopping1,myStopping2,myStopping3),
                           summary=any)


# Define some stopping rules
myStopping1 <- StoppingMinCohorts(nCohorts=3)
myStopping2 <- StoppingTargetProb(target=c(0.2, 0.35),
                                  prob=0.5)
myStopping3 <- StoppingMinPatients(nPatients=20)

# Create a list of stopping rules (of class 'StoppingList') which will then be
# summarized (in this specific example) with the 'any' function, meaning that the study 
# would be stopped if 'any' of the single stopping rules is TRUE.
mystopping <- StoppingList(stopList=c(myStopping1,myStopping2,myStopping3),
                           summary=any)

Initialization function for "StoppingMinCohorts"

Description

Initialization function for "StoppingMinCohorts"

Usage

StoppingMinCohorts(nCohorts)
StoppingMinCohorts(nCohorts)

Arguments

nCohorts

see StoppingMinCohorts

Value

the StoppingMinCohorts object

Stop based on minimum number of cohorts

Description

Stop based on minimum number of cohorts

Slots

nCohorts: minimum required number of cohorts

Examples


# As example, here is the rule for: 
#   stopping the study if at least 6 cohorts were already dosed

myStopping <- StoppingMinCohorts(nCohorts = 6)

# As example, here is the rule for: 
#   stopping the study if at least 6 cohorts were already dosed

myStopping <- StoppingMinCohorts(nCohorts = 6)

Initialization function for "StoppingMinPatients"

Description

Initialization function for "StoppingMinPatients"

Usage

StoppingMinPatients(nPatients)
StoppingMinPatients(nPatients)

Arguments

nPatients

see StoppingMinPatients

Value

the StoppingMinPatients object

Stop based on minimum number of patients

Description

Stop based on minimum number of patients

Slots

nPatients: minimum allowed number of patients

Examples


# As example, here is the rule for: 
#   stopping the study if at least 20 patients were already dosed

myStopping <- StoppingMinPatients(nPatients = 20)

# As example, here is the rule for: 
#   stopping the study if at least 20 patients were already dosed

myStopping <- StoppingMinPatients(nPatients = 20)

Initialization function for "StoppingMTDdistribution"

Description

Initialization function for "StoppingMTDdistribution"

Usage

StoppingMTDdistribution(target, thresh, prob)
StoppingMTDdistribution(target, thresh, prob)

Arguments

`target`	see `StoppingMTDdistribution`
`thresh`	see `StoppingMTDdistribution`
`prob`	see `StoppingMTDdistribution`

Value

the StoppingMTDdistribution object

Stop based on MTD distribution

Description

Has 90% probability above a threshold of 50% of the current MTD been reached? This class is used for this question.

Slots

target: the target toxicity probability (e.g. 0.33) defining the MTD
thresh: the threshold relative to the MTD (e.g. 0.5)
prob: required probability (e.g. 0.9)

Examples


# As example, here is the rule for: 
#   stopping the study if there is at least 0.9 probability that MTD > 0.5*next_dose.
#   Here MTD is defined as the dose for which prob(DLE)=0.33

myStopping <- StoppingMTDdistribution(target = 0.33,
                                      thresh = 0.5,
                                      prob = 0.9)

# As example, here is the rule for: 
#   stopping the study if there is at least 0.9 probability that MTD > 0.5*next_dose.
#   Here MTD is defined as the dose for which prob(DLE)=0.33

myStopping <- StoppingMTDdistribution(target = 0.33,
                                      thresh = 0.5,
                                      prob = 0.9)

Initialization function for "StoppingPatientsNearDose"

Description

Initialization function for "StoppingPatientsNearDose"

Usage

StoppingPatientsNearDose(nPatients, percentage)
StoppingPatientsNearDose(nPatients, percentage)

Arguments

`nPatients`	see `StoppingPatientsNearDose`
`percentage`	see `StoppingPatientsNearDose`

Value

the StoppingPatientsNearDose object

Stop based on number of patients near to next best dose

Description

Stop based on number of patients near to next best dose

Slots

nPatients: number of required patients
percentage: percentage (between 0 and 100) within the next best dose the patients must lie

Examples


# As example, here is the rule for: 
#   stopping the study if at least 9 patients were dosed at a dose within (1 +/- 0.2) 
#   of the next best dose

myStopping <- StoppingPatientsNearDose(nPatients = 9,
                                       percentage = 0.2)

# As example, here is the rule for: 
#   stopping the study if at least 9 patients were dosed at a dose within (1 +/- 0.2) 
#   of the next best dose

myStopping <- StoppingPatientsNearDose(nPatients = 9,
                                       percentage = 0.2)

Initialization function for "StoppingTargetBiomarker"

Description

Initialization function for "StoppingTargetBiomarker"

Usage

StoppingTargetBiomarker(target, scale = c("relative", "absolute"), prob)
StoppingTargetBiomarker(target, scale = c("relative", "absolute"), prob)

Arguments

`target`	see `StoppingTargetBiomarker`
`scale`	see `StoppingTargetBiomarker`
`prob`	see `StoppingTargetBiomarker`

Value

the StoppingTargetBiomarker object

Stop based on probability of target biomarker

Description

Stop based on probability of target biomarker

Slots

target: the biomarker target range, that needs to be reached. For example, (0.8, 1.0) and scale="relative" means we target a dose with at least 80% of maximum biomarker level.
scale: either relative (default, then the target is interpreted relative to the maximum, so must be a probability range) or absolute (then the target is interpreted as absolute biomarker range)
prob: required target probability for reaching sufficient precision

Examples

# As example, here is the rule for: 
#   stopping the study if there is at least 0.5 probability that the biomarker 
#   (efficacy) is within the biomarker target range of [0.9, 1.0] (relative to the
#   maximum for the biomarker).

myStopping <- StoppingTargetBiomarker(target = c(0.9, 1),
                                      prob = 0.5)

# As example, here is the rule for: 
#   stopping the study if there is at least 0.5 probability that the biomarker 
#   (efficacy) is within the biomarker target range of [0.9, 1.0] (relative to the
#   maximum for the biomarker).

myStopping <- StoppingTargetBiomarker(target = c(0.9, 1),
                                      prob = 0.5)

Initialization function for "StoppingTargetProb"

Description

Initialization function for "StoppingTargetProb"

Usage

StoppingTargetProb(target, prob)
StoppingTargetProb(target, prob)

Arguments

`target`	see `StoppingTargetProb`
`prob`	see `StoppingTargetProb`

Value

the StoppingTargetProb object

Stop based on probability of target tox interval

Description

Stop based on probability of target tox interval

Slots

target: the target toxicity interval, e.g. c(0.2, 0.35)
prob: required target toxicity probability (e.g. 0.4) for reaching sufficient precision

Examples

# As example, here is the rule for: 
#   stopping the study if the posterior probability that [0.2 =< Prob(DLT | dose) <= 0.35] 
#   for the next best dose is above 0.5

myStopping <- StoppingTargetProb(target=c(0.2, 0.35),
                                 prob=0.5)
# As example, here is the rule for: 
#   stopping the study if the posterior probability that [0.2 =< Prob(DLT | dose) <= 0.35] 
#   for the next best dose is above 0.5

myStopping <- StoppingTargetProb(target=c(0.2, 0.35),
                                 prob=0.5)

Initialization function for "StoppingTDCIRatio"

Description

Initialization function for "StoppingTDCIRatio"

Usage

StoppingTDCIRatio(targetRatio, targetEndOfTrial)
StoppingTDCIRatio(targetRatio, targetEndOfTrial)

Arguments

`targetRatio`	please refer to `StoppingTDCIRatio` class object
`targetEndOfTrial`	please refer to `StoppingTDCIRatio` class object

Value

the StoppingTDCIRatio class object

Stop based on a target ratio, the ratio of the upper to the lower 95% credibility interval of the estimate of TD end of trial, the dose with probability of DLE equals to the target probability of DLE used at the end of a trial

Description

Stop based on a target ratio, the ratio of the upper to the lower 95% credibility interval of the estimate of TD end of trial, the dose with probability of DLE equals to the target probability of DLE used at the end of a trial

Slots

targetRatio: the target ratio of the upper to the lower of the 95% credibility interval of the estimate that required to stop a trial
targetEndOfTrial: the target probability of DLE to be used at the end of a trial

Examples

##Define the target stopping ratio of 5 and 
##the target probability of DLE to be used at the end of a trial
##This is a ratio of the upper to the lower 95% credibility interval of the estimates
myStopping <- StoppingTDCIRatio(targetRatio=5,
                                targetEndOfTrial=0.3)
##Define the target stopping ratio of 5 and 
##the target probability of DLE to be used at the end of a trial
##This is a ratio of the upper to the lower 95% credibility interval of the estimates
myStopping <- StoppingTDCIRatio(targetRatio=5,
                                targetEndOfTrial=0.3)

Stop the trial?

Description

This function returns whether to stop the trial.

Usage

stopTrial(stopping, dose, samples, model, data, ...)

## S4 method for signature 'StoppingList,ANY,ANY,ANY,ANY'
stopTrial(stopping, dose, samples, model, data, ...)

## S4 method for signature 'StoppingAll,ANY,ANY,ANY,ANY'
stopTrial(stopping, dose, samples, model, data, ...)

## S4 method for signature 'StoppingAny,ANY,ANY,ANY,ANY'
stopTrial(stopping, dose, samples, model, data, ...)

## S4 method for signature 'StoppingCohortsNearDose,numeric,ANY,ANY,Data'
stopTrial(stopping, dose, samples, model, data, ...)

## S4 method for signature 'StoppingPatientsNearDose,numeric,ANY,ANY,Data'
stopTrial(stopping, dose, samples, model, data, ...)

## S4 method for signature 'StoppingMinCohorts,ANY,ANY,ANY,Data'
stopTrial(stopping, dose, samples, model, data, ...)

## S4 method for signature 'StoppingMinPatients,ANY,ANY,ANY,Data'
stopTrial(stopping, dose, samples, model, data, ...)

## S4 method for signature 'StoppingTargetProb,numeric,Samples,Model,ANY'
stopTrial(stopping, dose, samples, model, data, ...)

## S4 method for signature 'StoppingMTDdistribution,numeric,Samples,Model,ANY'
stopTrial(stopping, dose, samples, model, data, ...)

## S4 method for signature 
## 'StoppingTargetBiomarker,numeric,Samples,DualEndpoint,ANY'
stopTrial(stopping, dose, samples, model, data, ...)

## S4 method for signature 'StoppingHighestDose,numeric,ANY,ANY,Data'
stopTrial(stopping, dose, samples, model, data, ...)

## S4 method for signature 'StoppingTDCIRatio,ANY,Samples,ModelTox,ANY'
stopTrial(stopping, dose, samples, model, data, ...)

## S4 method for signature 'StoppingTDCIRatio,ANY,missing,ModelTox,ANY'
stopTrial(stopping, dose, samples, model, data, ...)

## S4 method for signature 'StoppingGstarCIRatio,ANY,Samples,ModelTox,DataDual'
stopTrial(
  stopping,
  dose,
  samples,
  model,
  data,
  TDderive,
  Effmodel,
  Effsamples,
  Gstarderive,
  ...
)

## S4 method for signature 'StoppingGstarCIRatio,ANY,missing,ModelTox,DataDual'
stopTrial(stopping, dose, model, data, Effmodel, ...)
stopTrial(stopping, dose, samples, model, data, ...)

## S4 method for signature 'StoppingList,ANY,ANY,ANY,ANY'
stopTrial(stopping, dose, samples, model, data, ...)

## S4 method for signature 'StoppingAll,ANY,ANY,ANY,ANY'
stopTrial(stopping, dose, samples, model, data, ...)

## S4 method for signature 'StoppingAny,ANY,ANY,ANY,ANY'
stopTrial(stopping, dose, samples, model, data, ...)

## S4 method for signature 'StoppingCohortsNearDose,numeric,ANY,ANY,Data'
stopTrial(stopping, dose, samples, model, data, ...)

## S4 method for signature 'StoppingPatientsNearDose,numeric,ANY,ANY,Data'
stopTrial(stopping, dose, samples, model, data, ...)

## S4 method for signature 'StoppingMinCohorts,ANY,ANY,ANY,Data'
stopTrial(stopping, dose, samples, model, data, ...)

## S4 method for signature 'StoppingMinPatients,ANY,ANY,ANY,Data'
stopTrial(stopping, dose, samples, model, data, ...)

## S4 method for signature 'StoppingTargetProb,numeric,Samples,Model,ANY'
stopTrial(stopping, dose, samples, model, data, ...)

## S4 method for signature 'StoppingMTDdistribution,numeric,Samples,Model,ANY'
stopTrial(stopping, dose, samples, model, data, ...)

## S4 method for signature 
## 'StoppingTargetBiomarker,numeric,Samples,DualEndpoint,ANY'
stopTrial(stopping, dose, samples, model, data, ...)

## S4 method for signature 'StoppingHighestDose,numeric,ANY,ANY,Data'
stopTrial(stopping, dose, samples, model, data, ...)

## S4 method for signature 'StoppingTDCIRatio,ANY,Samples,ModelTox,ANY'
stopTrial(stopping, dose, samples, model, data, ...)

## S4 method for signature 'StoppingTDCIRatio,ANY,missing,ModelTox,ANY'
stopTrial(stopping, dose, samples, model, data, ...)

## S4 method for signature 'StoppingGstarCIRatio,ANY,Samples,ModelTox,DataDual'
stopTrial(
  stopping,
  dose,
  samples,
  model,
  data,
  TDderive,
  Effmodel,
  Effsamples,
  Gstarderive,
  ...
)

## S4 method for signature 'StoppingGstarCIRatio,ANY,missing,ModelTox,DataDual'
stopTrial(stopping, dose, model, data, Effmodel, ...)

Arguments

`stopping`	The rule, an object of class `Stopping`
`dose`	the recommended next best dose
`samples`	the `Samples` object
`model`	The model input, an object of class `Model`
`data`	The data input, an object of class `Data`
`...`	additional arguments
`TDderive`	the function which derives from the input, a vector of the posterior samples called `TDsamples` of the dose which has the probability of the occurrence of DLE equals to either the targetDuringTrial or targetEndOfTrial, the final next best TDtargetDuringTrial (the dose with probability of the occurrence of DLE equals to the targetDuringTrial)and TDtargetEndOfTrial estimate.
`Effmodel`	the efficacy model of `ModelEff` class object
`Effsamples`	the efficacy samples of `Samples` class object
`Gstarderive`	the function which derives from the input, a vector of the posterior Gstar (the dose which gives the maximum gain value) samples called `Gstarsamples`, the final next best Gstar estimate.

Value

logical value: TRUE if the trial can be stopped, FALSE otherwise. It should have an attribute message which gives the reason for the decision.

Functions

stopTrial( stopping = StoppingList, dose = ANY, samples = ANY, model = ANY, data = ANY ): Stop based on multiple stopping rules
stopTrial( stopping = StoppingAll, dose = ANY, samples = ANY, model = ANY, data = ANY ): Stop based on fulfillment of all multiple stopping rules
stopTrial( stopping = StoppingAny, dose = ANY, samples = ANY, model = ANY, data = ANY ): Stop based on fulfillment of any stopping rule
stopTrial( stopping = StoppingCohortsNearDose, dose = numeric, samples = ANY, model = ANY, data = Data ): Stop based on number of cohorts near to next best dose
stopTrial( stopping = StoppingPatientsNearDose, dose = numeric, samples = ANY, model = ANY, data = Data ): Stop based on number of patients near to next best dose
stopTrial( stopping = StoppingMinCohorts, dose = ANY, samples = ANY, model = ANY, data = Data ): Stop based on minimum number of cohorts
stopTrial( stopping = StoppingMinPatients, dose = ANY, samples = ANY, model = ANY, data = Data ): Stop based on minimum number of patients
stopTrial( stopping = StoppingTargetProb, dose = numeric, samples = Samples, model = Model, data = ANY ): Stop based on probability of target tox interval
stopTrial( stopping = StoppingMTDdistribution, dose = numeric, samples = Samples, model = Model, data = ANY ): Stop based on MTD distribution
stopTrial( stopping = StoppingTargetBiomarker, dose = numeric, samples = Samples, model = DualEndpoint, data = ANY ): Stop based on probability of targeting biomarker
stopTrial( stopping = StoppingHighestDose, dose = numeric, samples = ANY, model = ANY, data = Data ): Stop when the highest dose is reached
stopTrial( stopping = StoppingTDCIRatio, dose = ANY, samples = Samples, model = ModelTox, data = ANY ): Stop based on 'StoppingTDCIRatio' class when reaching the target ratio of the upper to the lower 95 interval of the estimate (TDtargetEndOfTrial). This is a stopping rule which incorporate only DLE responses and DLE samples are given
stopTrial( stopping = StoppingTDCIRatio, dose = ANY, samples = missing, model = ModelTox, data = ANY ): Stop based on 'StoppingTDCIRatio' class when reaching the target ratio of the upper to the lower 95 interval of the estimate (TDtargetEndOfTrial). This is a stopping rule which incorporate only DLE responses and no DLE samples are involved
stopTrial( stopping = StoppingGstarCIRatio, dose = ANY, samples = Samples, model = ModelTox, data = DataDual ): Stop based on reaching the target ratio of the upper to the lower 95 interval of the estimate (the minimum of Gstar and TDtargetEndOfTrial). This is a stopping rule which incorporate DLE and efficacy responses and DLE and efficacy samples are also used.
stopTrial( stopping = StoppingGstarCIRatio, dose = ANY, samples = missing, model = ModelTox, data = DataDual ): Stop based on reaching the target ratio of the upper to the lower 95 interval of the estimate (the minimum of Gstar and TDtargetEndOfTrial). This is a stopping rule which incorporate DLE and efficacy responses without DLE and efficacy samples involved.

Examples


## Example of combining stopping rules with '&' and/or '|' operators

myStopping1 <- StoppingMinCohorts(nCohorts=3)
myStopping2 <- StoppingTargetProb(target=c(0.2, 0.35),
                                  prob=0.5)
myStopping3 <- StoppingMinPatients(nPatients=20)

myStopping <- (myStopping1 & myStopping2) | myStopping3





# Create some data
data <- Data(x=c(0.1, 0.5, 1.5, 3, 6, 10, 10, 10),
             y=c(0, 0, 0, 0, 0, 0, 1, 0),
             cohort=c(0, 1, 2, 3, 4, 5, 5, 5),
             doseGrid=
               c(0.1, 0.5, 1.5, 3, 6,
                 seq(from=10, to=80, by=2)))

# Initialize the CRM model used to model the data
model <- LogisticLogNormal(mean=c(-0.85, 1),
                           cov=
                             matrix(c(1, -0.5, -0.5, 1),
                                    nrow=2),
                           refDose=56)

# Set-up some MCMC parameters and generate samples from the posterior
options <- McmcOptions(burnin=100,
                       step=2,
                       samples=2000)
set.seed(94)
samples <- mcmc(data, model, options)

# Define the rule for dose increments and calculate the maximum dose allowed
myIncrements <- IncrementsRelative(intervals=c(0, 20),
                                   increments=c(1, 0.33))
nextMaxDose <- maxDose(myIncrements,
                       data=data)

# Define the rule which will be used to select the next best dose
# based on the class 'NextBestNCRM'
myNextBest <- NextBestNCRM(target=c(0.2, 0.35),
                           overdose=c(0.35, 1),
                           maxOverdoseProb=0.25)

# Calculate the next best dose
doseRecommendation <- nextBest(myNextBest,
                               doselimit=nextMaxDose,
                               samples=samples, model=model, data=data)

# Define the stopping rules
myStopping1 <- StoppingMinCohorts(nCohorts=3)
myStopping2 <- StoppingTargetProb(target=c(0.2, 0.35),
                                  prob=0.5)
myStopping3 <- StoppingMinPatients(nPatients=20)

# Create a list of stopping rules (of class 'StoppingList') which will then be
# summarized (in this specific example) with the 'any' function, meaning that the study 
# would be stopped if 'any' of the single stopping rules is TRUE.
mystopping <- StoppingList(stopList=c(myStopping1,myStopping2,myStopping3),
                           summary=any)

# Evaluate if to stop the Trial
stopTrial(stopping=myStopping, dose=doseRecommendation$value,
          samples=samples, model=model, data=data)




# Create some data
data <- Data(x=c(0.1, 0.5, 1.5, 3, 6, 10, 10, 10),
             y=c(0, 0, 0, 0, 0, 0, 1, 0),
             cohort=c(0, 1, 2, 3, 4, 5, 5, 5),
             doseGrid=
               c(0.1, 0.5, 1.5, 3, 6,
                 seq(from=10, to=80, by=2)))

# Initialize the CRM model used to model the data
model <- LogisticLogNormal(mean=c(-0.85, 1),
                           cov=
                             matrix(c(1, -0.5, -0.5, 1),
                                    nrow=2),
                           refDose=56)

# Set-up some MCMC parameters and generate samples from the posterior
options <- McmcOptions(burnin=100,
                       step=2,
                       samples=2000)
set.seed(94)
samples <- mcmc(data, model, options)

# Define the rule for dose increments and calculate the maximum dose allowed
myIncrements <- IncrementsRelative(intervals=c(0, 20),
                                   increments=c(1, 0.33))
nextMaxDose <- maxDose(myIncrements,
                       data=data)

# Define the rule which will be used to select the next best dose
# based on the class 'NextBestNCRM'
myNextBest <- NextBestNCRM(target=c(0.2, 0.35),
                           overdose=c(0.35, 1),
                           maxOverdoseProb=0.25)

# Calculate the next best dose
doseRecommendation <- nextBest(myNextBest,
                               doselimit=nextMaxDose,
                               samples=samples, model=model, data=data)

# Define the stopping rules
myStopping1 <- StoppingMinCohorts(nCohorts=3)
myStopping2 <- StoppingTargetProb(target=c(0.2, 0.35),
                                  prob=0.5)
myStopping3 <- StoppingMinPatients(nPatients=20)

# Combine the stopping rules, obtaining (in this specific example) a list of stopping 
# rules of class 'StoppingAll'
myStopping <- (myStopping1 | myStopping2) & myStopping3

# Evaluate if to stop the Trial
stopTrial(stopping=myStopping, dose=doseRecommendation$value,
          samples=samples, model=model, data=data)




# Create some data
data <- Data(x=c(0.1, 0.5, 1.5, 3, 6, 10, 10, 10),
             y=c(0, 0, 0, 0, 0, 0, 1, 0),
             cohort=c(0, 1, 2, 3, 4, 5, 5, 5),
             doseGrid=
               c(0.1, 0.5, 1.5, 3, 6,
                 seq(from=10, to=80, by=2)))

# Initialize the CRM model used to model the data
model <- LogisticLogNormal(mean=c(-0.85, 1),
                           cov=
                             matrix(c(1, -0.5, -0.5, 1),
                                    nrow=2),
                           refDose=56)

# Set-up some MCMC parameters and generate samples from the posterior
options <- McmcOptions(burnin=100,
                       step=2,
                       samples=2000)
set.seed(94)
samples <- mcmc(data, model, options)

# Define the rule for dose increments and calculate the maximum dose allowed
myIncrements <- IncrementsRelative(intervals=c(0, 20),
                                   increments=c(1, 0.33))
nextMaxDose <- maxDose(myIncrements,
                       data=data)

# Define the rule which will be used to select the next best dose
# based on the class 'NextBestNCRM'
myNextBest <- NextBestNCRM(target=c(0.2, 0.35),
                           overdose=c(0.35, 1),
                           maxOverdoseProb=0.25)

# Calculate the next best dose
doseRecommendation <- nextBest(myNextBest,
                               doselimit=nextMaxDose,
                               samples=samples, model=model, data=data)

# Define the stopping rules
myStopping1 <- StoppingMinCohorts(nCohorts=3)
myStopping2 <- StoppingTargetProb(target=c(0.2, 0.35),
                                  prob=0.5)
myStopping3 <- StoppingMinPatients(nPatients=20)

# Combine the stopping rules, obtaining (in this specific example) a list of stopping 
# rules of class 'StoppingAny'
myStopping <- (myStopping1 | myStopping2) | myStopping3

# Evaluate if to stop the Trial
stopTrial(stopping=myStopping, dose=doseRecommendation$value,
          samples=samples, model=model, data=data)




# Create the data
data <- Data(x=c(0.1, 0.5, 1.5, 3, 6, 10, 10, 10),
             y=c(0, 0, 0, 0, 0, 0, 1, 0),
             cohort=c(0, 1, 2, 3, 4, 5, 5, 5),
             doseGrid=
               c(0.1, 0.5, 1.5, 3, 6,
                 seq(from=10, to=80, by=2)))

# Initialize the CRM model used to model the data
model <- LogisticLogNormal(mean=c(-0.85, 1),
                           cov=
                             matrix(c(1, -0.5, -0.5, 1),
                                    nrow=2),
                           refDose=56)

# Set-up some MCMC parameters and generate samples from the posterior
options <- McmcOptions(burnin=100,
                       step=2,
                       samples=2000)
set.seed(94)
samples <- mcmc(data, model, options)

# Define the rule for dose increments and calculate the maximum dose allowed
myIncrements <- IncrementsRelative(intervals=c(0, 20),
                                   increments=c(1, 0.33))
nextMaxDose <- maxDose(myIncrements,
                       data=data)

# Define the rule which will be used to select the next best dose
# based on the class 'NextBestNCRM'
myNextBest <- NextBestNCRM(target=c(0.2, 0.35),
                           overdose=c(0.35, 1),
                           maxOverdoseProb=0.25)

# Calculate the next best dose
doseRecommendation <- nextBest(myNextBest,
                               doselimit=nextMaxDose,
                               samples=samples, model=model, data=data)

# Define the stopping rule such that the study would be stopped if at least 3 
# cohorts were already dosed within 1 +/- 0.2 of the next best dose
myStopping <- StoppingCohortsNearDose(nCohorts = 3,
                                      percentage = 0.2)

# Evaluate if to stop the trial
stopTrial(stopping=myStopping, 
          dose=doseRecommendation$value,
          data=data)


# Create the data
data <- Data(x=c(0.1, 0.5, 1.5, 3, 6, 10, 10, 10),
             y=c(0, 0, 0, 0, 0, 0, 1, 0),
             cohort=c(0, 1, 2, 3, 4, 5, 5, 5),
             doseGrid=
               c(0.1, 0.5, 1.5, 3, 6,
                 seq(from=10, to=80, by=2)))

# Initialize the CRM model used to model the data
model <- LogisticLogNormal(mean=c(-0.85, 1),
                           cov=
                             matrix(c(1, -0.5, -0.5, 1),
                                    nrow=2),
                           refDose=56)

# Set-up some MCMC parameters and generate samples from the posterior
options <- McmcOptions(burnin=100,
                       step=2,
                       samples=2000)
set.seed(94)
samples <- mcmc(data, model, options)

# Define the rule for dose increments and calculate the maximum dose allowed
myIncrements <- IncrementsRelative(intervals=c(0, 20),
                                   increments=c(1, 0.33))
nextMaxDose <- maxDose(myIncrements,
                       data=data)

# Define the rule which will be used to select the next best dose
# based on the class 'NextBestNCRM'
myNextBest <- NextBestNCRM(target=c(0.2, 0.35),
                           overdose=c(0.35, 1),
                           maxOverdoseProb=0.25)

# Calculate the next best dose
doseRecommendation <- nextBest(myNextBest,
                               doselimit=nextMaxDose,
                               samples=samples, model=model, data=data)

# Define the stopping rule such that the study would be stopped if at least 9 
# patients were already dosed within 1 +/- 0.2 of the next best dose
myStopping <- StoppingPatientsNearDose(nPatients = 9,
                                       percentage = 0.2)

# Evaluate if to stop the trial
stopTrial(stopping=myStopping, 
          dose=doseRecommendation$value,
          data=data)


# Create the data
data <- Data(x=c(0.1, 0.5, 1.5, 3, 6, 10, 10, 10),
             y=c(0, 0, 0, 0, 0, 0, 1, 0),
             cohort=c(0, 1, 2, 3, 4, 5, 5, 5),
             doseGrid=
               c(0.1, 0.5, 1.5, 3, 6,
                 seq(from=10, to=80, by=2)))

# Initialize the CRM model used to model the data
model <- LogisticLogNormal(mean=c(-0.85, 1),
                           cov=
                             matrix(c(1, -0.5, -0.5, 1),
                                    nrow=2),
                           refDose=56)

# Set-up some MCMC parameters and generate samples from the posterior
options <- McmcOptions(burnin=100,
                       step=2,
                       samples=2000)
set.seed(94)
samples <- mcmc(data, model, options)

# Define the rule for dose increments and calculate the maximum dose allowed
myIncrements <- IncrementsRelative(intervals=c(0, 20),
                                   increments=c(1, 0.33))
nextMaxDose <- maxDose(myIncrements,
                       data=data)

# Define the rule which will be used to select the next best dose
# based on the class 'NextBestNCRM'
myNextBest <- NextBestNCRM(target=c(0.2, 0.35),
                           overdose=c(0.35, 1),
                           maxOverdoseProb=0.25)

# Calculate the next best dose
doseRecommendation <- nextBest(myNextBest,
                               doselimit=nextMaxDose,
                               samples=samples, model=model, data=data)

# Define the stopping rule such that the study would be stopped if at least 6 
# cohorts were already dosed 
myStopping <- StoppingMinCohorts(nCohorts = 6)

# Evaluate if to stop the trial
stopTrial(stopping=myStopping, 
          dose=doseRecommendation$value,
          data=data)


# Create the data
data <- Data(x=c(0.1, 0.5, 1.5, 3, 6, 10, 10, 10),
             y=c(0, 0, 0, 0, 0, 0, 1, 0),
             cohort=c(0, 1, 2, 3, 4, 5, 5, 5),
             doseGrid=
               c(0.1, 0.5, 1.5, 3, 6,
                 seq(from=10, to=80, by=2)))

# Initialize the CRM model used to model the data
model <- LogisticLogNormal(mean=c(-0.85, 1),
                           cov=
                             matrix(c(1, -0.5, -0.5, 1),
                                    nrow=2),
                           refDose=56)

# Set-up some MCMC parameters and generate samples from the posterior
options <- McmcOptions(burnin=100,
                       step=2,
                       samples=2000)
set.seed(94)
samples <- mcmc(data, model, options)

# Define the rule for dose increments and calculate the maximum dose allowed
myIncrements <- IncrementsRelative(intervals=c(0, 20),
                                   increments=c(1, 0.33))
nextMaxDose <- maxDose(myIncrements,
                       data=data)

# Define the rule which will be used to select the next best dose
# based on the class 'NextBestNCRM'
myNextBest <- NextBestNCRM(target=c(0.2, 0.35),
                           overdose=c(0.35, 1),
                           maxOverdoseProb=0.25)

# Calculate the next best dose
doseRecommendation <- nextBest(myNextBest,
                               doselimit=nextMaxDose,
                               samples=samples, model=model, data=data)

# Define the stopping rule such that the study would be stopped if at least 20 
# patients were already dosed 
myStopping <- StoppingMinPatients(nPatients = 20)

# Evaluate if to stop the trial
stopTrial(stopping=myStopping, 
          dose=doseRecommendation$value,
          data=data)


# Create the data
data <- Data(x=c(0.1, 0.5, 1.5, 3, 6, 10, 10, 10),
             y=c(0, 0, 0, 0, 0, 0, 1, 0),
             cohort=c(0, 1, 2, 3, 4, 5, 5, 5),
             doseGrid=
               c(0.1, 0.5, 1.5, 3, 6,
                 seq(from=10, to=80, by=2)))

# Initialize the CRM model used to model the data
model <- LogisticLogNormal(mean=c(-0.85, 1),
                           cov=
                             matrix(c(1, -0.5, -0.5, 1),
                                    nrow=2),
                           refDose=56)

# Set-up some MCMC parameters and generate samples from the posterior
options <- McmcOptions(burnin=100,
                       step=2,
                       samples=2000)
set.seed(94)
samples <- mcmc(data, model, options)

# Define the rule for dose increments and calculate the maximum dose allowed
myIncrements <- IncrementsRelative(intervals=c(0, 20),
                                   increments=c(1, 0.33))
nextMaxDose <- maxDose(myIncrements,
                       data=data)

# Define the rule which will be used to select the next best dose
# based on the class 'NextBestNCRM'
myNextBest <- NextBestNCRM(target=c(0.2, 0.35),
                           overdose=c(0.35, 1),
                           maxOverdoseProb=0.25)

# Calculate the next best dose
doseRecommendation <- nextBest(myNextBest,
                               doselimit=nextMaxDose,
                               samples=samples, model=model, data=data)

# Define the stopping rule such that the study would be stopped if there is at least 
# 0.5 posterior probability that [0.2 =< Prob(DLT | next-best-dose) <= 0.35] 
myStopping <- StoppingTargetProb(target=c(0.2, 0.35),
                                 prob=0.5)

# Evaluate if to stop the trial
stopTrial(stopping=myStopping, 
          dose=doseRecommendation$value,
          samples=samples,
          model=model,
          data=data)


# Create the data
data <- Data(x=c(0.1, 0.5, 1.5, 3, 6, 10, 10, 10),
             y=c(0, 0, 0, 0, 0, 0, 1, 0),
             cohort=c(0, 1, 2, 3, 4, 5, 5, 5),
             doseGrid=
               c(0.1, 0.5, 1.5, 3, 6,
                 seq(from=10, to=80, by=2)))

# Initialize the CRM model used to model the data
model <- LogisticLogNormal(mean=c(-0.85, 1),
                           cov=
                             matrix(c(1, -0.5, -0.5, 1),
                                    nrow=2),
                           refDose=56)

# Set-up some MCMC parameters and generate samples from the posterior
options <- McmcOptions(burnin=100,
                       step=2,
                       samples=2000)
set.seed(94)
samples <- mcmc(data, model, options)

# Define the rule for dose increments and calculate the maximum dose allowed
myIncrements <- IncrementsRelative(intervals=c(0, 20),
                                   increments=c(1, 0.33))
nextMaxDose <- maxDose(myIncrements,
                       data=data)

# Define the rule which will be used to select the next best dose
# based on the class 'NextBestNCRM'
myNextBest <- NextBestNCRM(target=c(0.2, 0.35),
                           overdose=c(0.35, 1),
                           maxOverdoseProb=0.25)

# Calculate the next best dose
doseRecommendation <- nextBest(myNextBest,
                               doselimit=nextMaxDose,
                               samples=samples, model=model, data=data)

# Define the stopping rule such that the study would be stopped if there is at least 
# 0.9 probability that MTD > 0.5*next_best_dose. Here MTD is defined as the dose for 
# which prob(DLE)=0.33
myStopping <- StoppingMTDdistribution(target = 0.33,
                                      thresh = 0.5,
                                      prob = 0.9)

# Evaluate if to stop the trial
stopTrial(stopping=myStopping, 
          dose=doseRecommendation$value,
          samples=samples,
          model=model,
          data=data)


# Create the data
data <- DataDual(
  x=c(0.1, 0.5, 1.5, 3, 6, 10, 10, 10,
      20, 20, 20, 40, 40, 40, 50, 50, 50),
  y=c(0, 0, 0, 0, 0, 0, 1, 0,
      0, 1, 1, 0, 0, 1, 0, 1, 1),
  w=c(0.31, 0.42, 0.59, 0.45, 0.6, 0.7, 0.55, 0.6,
      0.52, 0.54, 0.56, 0.43, 0.41, 0.39, 0.34, 0.38, 0.21),
  doseGrid=c(0.1, 0.5, 1.5, 3, 6,
             seq(from=10, to=80, by=2)))

# Initialize the Dual-Endpoint model (in this case RW1)
model <- DualEndpointRW(mu = c(0, 1),
                        Sigma = matrix(c(1, 0, 0, 1), nrow=2),
                        sigma2betaW = 0.01,
                        sigma2W = c(a=0.1, b=0.1),
                        rho = c(a=1, b=1),
                        smooth = "RW1")

# Set-up some MCMC parameters and generate samples from the posterior
options <- McmcOptions(burnin=100,
                       step=2,
                       samples=500)
set.seed(94)
samples <- mcmc(data, model, options)

# Define the rule for dose increments and calculate the maximum dose allowed
myIncrements <- IncrementsRelative(intervals=c(0, 20),
                                   increments=c(1, 0.33))
nextMaxDose <- maxDose(myIncrements,
                       data=data)

# Define the rule which will be used to select the next best dose
# In this case target a dose achieving at least 0.9 of maximum biomarker level (efficacy)
# and with a probability below 0.25 that prob(DLT)>0.35 (safety)
myNextBest <- NextBestDualEndpoint(target=c(0.9, 1),
                                   overdose=c(0.35, 1),
                                   maxOverdoseProb=0.25)

# Calculate the next best dose
doseRecommendation <- nextBest(myNextBest,
                               doselimit=nextMaxDose,
                               samples=samples,
                               model=model,
                               data=data)

# Define the stopping rule such that the study would be stopped if if there is at 
# least 0.5 posterior probability that the biomarker (efficacy) is within the 
# biomarker target range of [0.9, 1.0] (relative to the maximum for the biomarker). 
myStopping <- StoppingTargetBiomarker(target = c(0.9, 1),
                                      prob = 0.5)

# Evaluate if to stop the trial
stopTrial(stopping=myStopping, 
          dose=doseRecommendation$value,
          samples=samples,
          model=model,
          data=data)

# Create the data
data <- Data(x=c(0.1, 0.5, 1.5, 3, 6, 10, 10, 10, 20, 20, 20, 40, 40, 40,
                 80, 80, 80),
             y=c(0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0),
             cohort=c(0, 1, 2, 3, 4, 5, 5, 5, 6, 6, 6, 7, 7, 7, 8, 8, 8),
             doseGrid=
               c(0.1, 0.5, 1.5, 3, 6,
                 seq(from=10, to=80, by=2)))

# Initialize the CRM model used to model the data
model <- LogisticLogNormal(mean=c(-0.85, 1),
                           cov=
                             matrix(c(1, -0.5, -0.5, 1),
                                    nrow=2),
                           refDose=56)

# Set-up some MCMC parameters and generate samples from the posterior
options <- McmcOptions(burnin=100,
                       step=2,
                       samples=2000)
set.seed(94)
samples <- mcmc(data, model, options)

# Define the rule for dose increments and calculate the maximum dose allowed
myIncrements <- IncrementsRelative(intervals=c(0, 20),
                                   increments=c(1, 0.33))
nextMaxDose <- maxDose(myIncrements,
                       data=data)

# Define the rule which will be used to select the next best dose
# based on the class 'NextBestNCRM'
myNextBest <- NextBestNCRM(target=c(0.2, 0.35),
                           overdose=c(0.35, 1),
                           maxOverdoseProb=0.25)

# Calculate the next best dose
doseRecommendation <- nextBest(myNextBest,
                               doselimit=nextMaxDose,
                               samples=samples, model=model, data=data)

# Define the stopping rule such that the study would be stopped if there is at least 
# 0.5 posterior probability that [0.2 =< Prob(DLT | next-best-dose) <= 0.35] 
stopTarget <- StoppingTargetProb(target=c(0.2, 0.35),
                                 prob=0.5)

## now use the StoppingHighestDose rule:
stopHigh <-
  StoppingHighestDose() &
  StoppingPatientsNearDose(nPatients=3, percentage=0) &
  StoppingTargetProb(target=c(0, 0.2),
                     prob=0.5)

## and combine everything:
myStopping <- stopTarget | stopHigh

# Then evaluate if to stop the trial
stopTrial(stopping=myStopping, 
          dose=doseRecommendation$value,
          samples=samples,
          model=model,
          data=data)



##define the stopping rules based on the 'StoppingTDCIRatio' class
##Using only DLE responses with samples
## we need a data object with doses >= 1:
data<-Data(x=c(25,50,50,75,150,200,225,300),
           y=c(0,0,0,0,1,1,1,1),
           doseGrid=seq(from=25,to=300,by=25))

##model can be specified of 'Model' or 'ModelTox' class
##For example, the 'logisticIndepBeta' class model
model<-LogisticIndepBeta(binDLE=c(1.05,1.8),DLEweights=c(3,3),DLEdose=c(25,300),data=data)
##define MCMC options
##for illustration purpose we use 10 burn-in and generate 50 samples
options<-McmcOptions(burnin=10,step=2,samples=50)
##samples of 'Samples' class
samples<-mcmc(data,model,options)
##define the 'StoppingTDCIRatio' class
myStopping <- StoppingTDCIRatio(targetRatio=5,
                                       targetEndOfTrial=0.3)
##Find the next Recommend dose using the nextBest method (plesae refer to nextbest examples)
tdNextBest<-NextBestTDsamples(targetDuringTrial=0.35,targetEndOfTrial=0.3,
                              derive=function(TDsamples){quantile(TDsamples,probs=0.3)})


RecommendDose<-nextBest(tdNextBest,doselimit=max(data@doseGrid),samples=samples,
                        model=model,data=data)
##use 'stopTrial' to determine if the rule has been fulfilled
##use 0.3 as the target proability of DLE at the end of the trial

stopTrial(stopping=myStopping,dose=RecommendDose$nextdose,
          samples=samples,model=model,data=data)
## RecommendDose$nextdose refers to the next dose obtained in RecommendDose
##define the stopping rules based on the 'StoppingTDCIRatio' class
##Using only DLE responses 
## we need a data object with doses >= 1:
data<-Data(x=c(25,50,50,75,150,200,225,300),
           y=c(0,0,0,0,1,1,1,1),
           doseGrid=seq(from=25,to=300,by=25))

##model must be of 'ModelTox' class
##For example, the 'logisticIndepBeta' class model
model<-LogisticIndepBeta(binDLE=c(1.05,1.8),DLEweights=c(3,3),DLEdose=c(25,300),data=data)
##define the 'StoppingTDCIRatio' class
myStopping <- StoppingTDCIRatio(targetRatio=5,
                                targetEndOfTrial=0.3)
##Find the next Recommend dose using the nextBest method (plesae refer to nextbest examples)
tdNextBest<-NextBestTD(targetDuringTrial=0.35,targetEndOfTrial=0.3)


RecommendDose<-nextBest(tdNextBest,doselimit=max(data@doseGrid),model=model,data=data)
##use 'stopTrial' to determine if the rule has been fulfilled
##use 0.3 as the target proability of DLE at the end of the trial

stopTrial(stopping=myStopping,dose=RecommendDose$nextdose,
          model=model,data=data)
## RecommendDose$nextdose refers to the next dose obtained in RecommendDose
##define the stopping rules based on the 'StoppingGstarCIRatio' class
##Using both DLE and efficacy responses
## we need a data object with doses >= 1:
data <-DataDual(x=c(25,50,25,50,75,300,250,150),
                y=c(0,0,0,0,0,1,1,0),
                w=c(0.31,0.42,0.59,0.45,0.6,0.7,0.6,0.52),
                doseGrid=seq(25,300,25),
                placebo=FALSE)

##DLEmodel must be of 'ModelTox' class
##For example, the 'logisticIndepBeta' class model
DLEmodel<-LogisticIndepBeta(binDLE=c(1.05,1.8),DLEweights=c(3,3),DLEdose=c(25,300),data=data)

##Effmodel must be  of 'ModelEff' class
##For example, the 'Effloglog' class model
Effmodel<-Effloglog(Eff=c(1.223,2.513),Effdose=c(25,300),nu=c(a=1,b=0.025),data=data,c=0)
##for illustration purpose we use 10 burn-in and generate 50 samples
options<-McmcOptions(burnin=10,step=2,samples=50)
##DLE and efficacy samples must be of 'Samples' class
DLEsamples<-mcmc(data,DLEmodel,options)
Effsamples<-mcmc(data,Effmodel,options)

##define the 'StoppingGstarCIRatio' class
myStopping <- StoppingGstarCIRatio(targetRatio=5,
                                          targetEndOfTrial=0.3)
##Find the next Recommend dose using the nextBest method (plesae refer to nextbest examples)
mynextbest<-NextBestMaxGainSamples(DLEDuringTrialtarget=0.35,
                                   DLEEndOfTrialtarget=0.3,
                                   TDderive=function(TDsamples){
                                     quantile(TDsamples,prob=0.3)},
                                   Gstarderive=function(Gstarsamples){
                                     quantile(Gstarsamples,prob=0.5)})

RecommendDose<-nextBest(mynextbest,doselimit=max(data@doseGrid),samples=DLEsamples,model=DLEmodel,
                        data=data,Effmodel=Effmodel,Effsamples=Effsamples)
##use 'stopTrial' to determine if the rule has been fulfilled
##use 0.3 as the target proability of DLE at the end of the trial 

stopTrial(stopping=myStopping,
          dose=RecommendDose$nextdose,
          samples=DLEsamples,
          model=DLEmodel,
          data=data,
          TDderive=function(TDsamples){
            quantile(TDsamples,prob=0.3)},
          Effmodel=Effmodel,
          Effsamples=Effsamples,
          Gstarderive=function(Gstarsamples){
            quantile(Gstarsamples,prob=0.5)})

## RecommendDose$nextdose refers to the next dose obtained in RecommendDose
##define the stopping rules based on the 'StoppingGstarCIRatio' class
##Using both DLE and efficacy responses
## we need a data object with doses >= 1:
data <-DataDual(x=c(25,50,25,50,75,300,250,150),
                y=c(0,0,0,0,0,1,1,0),
                w=c(0.31,0.42,0.59,0.45,0.6,0.7,0.6,0.52),
                doseGrid=seq(25,300,25),
                placebo=FALSE)

##DLEmodel must be of 'ModelTox' class
##For example, the 'logisticIndepBeta' class model
DLEmodel<-LogisticIndepBeta(binDLE=c(1.05,1.8),DLEweights=c(3,3),DLEdose=c(25,300),data=data)

##Effmodel must be  of 'ModelEff' class
##For example, the 'Effloglog' class model
Effmodel<-Effloglog(Eff=c(1.223,2.513),Effdose=c(25,300),nu=c(a=1,b=0.025),data=data,c=0)


##define the 'StoppingGstarCIRatio' class
myStopping <- StoppingGstarCIRatio(targetRatio=5,
                                   targetEndOfTrial=0.3)
##Find the next Recommend dose using the nextBest method (plesae refer to nextbest examples)
mynextbest<-NextBestMaxGain(DLEDuringTrialtarget=0.35,DLEEndOfTrialtarget=0.3)

RecommendDose<-nextBest(mynextbest,doselimit=max(data@doseGrid),model=DLEmodel,
                        Effmodel=Effmodel,data=data)

##use 'stopTrial' to determine if the rule has been fulfilled
##use 0.3 as the target proability of DLE at the end of the trial


stopTrial(stopping=myStopping,dose=RecommendDose$nextdose,model=DLEmodel,
          data=data, Effmodel=Effmodel)


## RecommendDose$nextdose refers to the next dose obtained in RecommendDose
## Example of combining stopping rules with '&' and/or '|' operators

myStopping1 <- StoppingMinCohorts(nCohorts=3)
myStopping2 <- StoppingTargetProb(target=c(0.2, 0.35),
                                  prob=0.5)
myStopping3 <- StoppingMinPatients(nPatients=20)

myStopping <- (myStopping1 & myStopping2) | myStopping3





# Create some data
data <- Data(x=c(0.1, 0.5, 1.5, 3, 6, 10, 10, 10),
             y=c(0, 0, 0, 0, 0, 0, 1, 0),
             cohort=c(0, 1, 2, 3, 4, 5, 5, 5),
             doseGrid=
               c(0.1, 0.5, 1.5, 3, 6,
                 seq(from=10, to=80, by=2)))

# Initialize the CRM model used to model the data
model <- LogisticLogNormal(mean=c(-0.85, 1),
                           cov=
                             matrix(c(1, -0.5, -0.5, 1),
                                    nrow=2),
                           refDose=56)

# Set-up some MCMC parameters and generate samples from the posterior
options <- McmcOptions(burnin=100,
                       step=2,
                       samples=2000)
set.seed(94)
samples <- mcmc(data, model, options)

# Define the rule for dose increments and calculate the maximum dose allowed
myIncrements <- IncrementsRelative(intervals=c(0, 20),
                                   increments=c(1, 0.33))
nextMaxDose <- maxDose(myIncrements,
                       data=data)

# Define the rule which will be used to select the next best dose
# based on the class 'NextBestNCRM'
myNextBest <- NextBestNCRM(target=c(0.2, 0.35),
                           overdose=c(0.35, 1),
                           maxOverdoseProb=0.25)

# Calculate the next best dose
doseRecommendation <- nextBest(myNextBest,
                               doselimit=nextMaxDose,
                               samples=samples, model=model, data=data)

# Define the stopping rules
myStopping1 <- StoppingMinCohorts(nCohorts=3)
myStopping2 <- StoppingTargetProb(target=c(0.2, 0.35),
                                  prob=0.5)
myStopping3 <- StoppingMinPatients(nPatients=20)

# Create a list of stopping rules (of class 'StoppingList') which will then be
# summarized (in this specific example) with the 'any' function, meaning that the study 
# would be stopped if 'any' of the single stopping rules is TRUE.
mystopping <- StoppingList(stopList=c(myStopping1,myStopping2,myStopping3),
                           summary=any)

# Evaluate if to stop the Trial
stopTrial(stopping=myStopping, dose=doseRecommendation$value,
          samples=samples, model=model, data=data)




# Create some data
data <- Data(x=c(0.1, 0.5, 1.5, 3, 6, 10, 10, 10),
             y=c(0, 0, 0, 0, 0, 0, 1, 0),
             cohort=c(0, 1, 2, 3, 4, 5, 5, 5),
             doseGrid=
               c(0.1, 0.5, 1.5, 3, 6,
                 seq(from=10, to=80, by=2)))

# Initialize the CRM model used to model the data
model <- LogisticLogNormal(mean=c(-0.85, 1),
                           cov=
                             matrix(c(1, -0.5, -0.5, 1),
                                    nrow=2),
                           refDose=56)

# Set-up some MCMC parameters and generate samples from the posterior
options <- McmcOptions(burnin=100,
                       step=2,
                       samples=2000)
set.seed(94)
samples <- mcmc(data, model, options)

# Define the rule for dose increments and calculate the maximum dose allowed
myIncrements <- IncrementsRelative(intervals=c(0, 20),
                                   increments=c(1, 0.33))
nextMaxDose <- maxDose(myIncrements,
                       data=data)

# Define the rule which will be used to select the next best dose
# based on the class 'NextBestNCRM'
myNextBest <- NextBestNCRM(target=c(0.2, 0.35),
                           overdose=c(0.35, 1),
                           maxOverdoseProb=0.25)

# Calculate the next best dose
doseRecommendation <- nextBest(myNextBest,
                               doselimit=nextMaxDose,
                               samples=samples, model=model, data=data)

# Define the stopping rules
myStopping1 <- StoppingMinCohorts(nCohorts=3)
myStopping2 <- StoppingTargetProb(target=c(0.2, 0.35),
                                  prob=0.5)
myStopping3 <- StoppingMinPatients(nPatients=20)

# Combine the stopping rules, obtaining (in this specific example) a list of stopping 
# rules of class 'StoppingAll'
myStopping <- (myStopping1 | myStopping2) & myStopping3

# Evaluate if to stop the Trial
stopTrial(stopping=myStopping, dose=doseRecommendation$value,
          samples=samples, model=model, data=data)




# Create some data
data <- Data(x=c(0.1, 0.5, 1.5, 3, 6, 10, 10, 10),
             y=c(0, 0, 0, 0, 0, 0, 1, 0),
             cohort=c(0, 1, 2, 3, 4, 5, 5, 5),
             doseGrid=
               c(0.1, 0.5, 1.5, 3, 6,
                 seq(from=10, to=80, by=2)))

# Initialize the CRM model used to model the data
model <- LogisticLogNormal(mean=c(-0.85, 1),
                           cov=
                             matrix(c(1, -0.5, -0.5, 1),
                                    nrow=2),
                           refDose=56)

# Set-up some MCMC parameters and generate samples from the posterior
options <- McmcOptions(burnin=100,
                       step=2,
                       samples=2000)
set.seed(94)
samples <- mcmc(data, model, options)

# Define the rule for dose increments and calculate the maximum dose allowed
myIncrements <- IncrementsRelative(intervals=c(0, 20),
                                   increments=c(1, 0.33))
nextMaxDose <- maxDose(myIncrements,
                       data=data)

# Define the rule which will be used to select the next best dose
# based on the class 'NextBestNCRM'
myNextBest <- NextBestNCRM(target=c(0.2, 0.35),
                           overdose=c(0.35, 1),
                           maxOverdoseProb=0.25)

# Calculate the next best dose
doseRecommendation <- nextBest(myNextBest,
                               doselimit=nextMaxDose,
                               samples=samples, model=model, data=data)

# Define the stopping rules
myStopping1 <- StoppingMinCohorts(nCohorts=3)
myStopping2 <- StoppingTargetProb(target=c(0.2, 0.35),
                                  prob=0.5)
myStopping3 <- StoppingMinPatients(nPatients=20)

# Combine the stopping rules, obtaining (in this specific example) a list of stopping 
# rules of class 'StoppingAny'
myStopping <- (myStopping1 | myStopping2) | myStopping3

# Evaluate if to stop the Trial
stopTrial(stopping=myStopping, dose=doseRecommendation$value,
          samples=samples, model=model, data=data)




# Create the data
data <- Data(x=c(0.1, 0.5, 1.5, 3, 6, 10, 10, 10),
             y=c(0, 0, 0, 0, 0, 0, 1, 0),
             cohort=c(0, 1, 2, 3, 4, 5, 5, 5),
             doseGrid=
               c(0.1, 0.5, 1.5, 3, 6,
                 seq(from=10, to=80, by=2)))

# Initialize the CRM model used to model the data
model <- LogisticLogNormal(mean=c(-0.85, 1),
                           cov=
                             matrix(c(1, -0.5, -0.5, 1),
                                    nrow=2),
                           refDose=56)

# Set-up some MCMC parameters and generate samples from the posterior
options <- McmcOptions(burnin=100,
                       step=2,
                       samples=2000)
set.seed(94)
samples <- mcmc(data, model, options)

# Define the rule for dose increments and calculate the maximum dose allowed
myIncrements <- IncrementsRelative(intervals=c(0, 20),
                                   increments=c(1, 0.33))
nextMaxDose <- maxDose(myIncrements,
                       data=data)

# Define the rule which will be used to select the next best dose
# based on the class 'NextBestNCRM'
myNextBest <- NextBestNCRM(target=c(0.2, 0.35),
                           overdose=c(0.35, 1),
                           maxOverdoseProb=0.25)

# Calculate the next best dose
doseRecommendation <- nextBest(myNextBest,
                               doselimit=nextMaxDose,
                               samples=samples, model=model, data=data)

# Define the stopping rule such that the study would be stopped if at least 3 
# cohorts were already dosed within 1 +/- 0.2 of the next best dose
myStopping <- StoppingCohortsNearDose(nCohorts = 3,
                                      percentage = 0.2)

# Evaluate if to stop the trial
stopTrial(stopping=myStopping, 
          dose=doseRecommendation$value,
          data=data)


# Create the data
data <- Data(x=c(0.1, 0.5, 1.5, 3, 6, 10, 10, 10),
             y=c(0, 0, 0, 0, 0, 0, 1, 0),
             cohort=c(0, 1, 2, 3, 4, 5, 5, 5),
             doseGrid=
               c(0.1, 0.5, 1.5, 3, 6,
                 seq(from=10, to=80, by=2)))

# Initialize the CRM model used to model the data
model <- LogisticLogNormal(mean=c(-0.85, 1),
                           cov=
                             matrix(c(1, -0.5, -0.5, 1),
                                    nrow=2),
                           refDose=56)

# Set-up some MCMC parameters and generate samples from the posterior
options <- McmcOptions(burnin=100,
                       step=2,
                       samples=2000)
set.seed(94)
samples <- mcmc(data, model, options)

# Define the rule for dose increments and calculate the maximum dose allowed
myIncrements <- IncrementsRelative(intervals=c(0, 20),
                                   increments=c(1, 0.33))
nextMaxDose <- maxDose(myIncrements,
                       data=data)

# Define the rule which will be used to select the next best dose
# based on the class 'NextBestNCRM'
myNextBest <- NextBestNCRM(target=c(0.2, 0.35),
                           overdose=c(0.35, 1),
                           maxOverdoseProb=0.25)

# Calculate the next best dose
doseRecommendation <- nextBest(myNextBest,
                               doselimit=nextMaxDose,
                               samples=samples, model=model, data=data)

# Define the stopping rule such that the study would be stopped if at least 9 
# patients were already dosed within 1 +/- 0.2 of the next best dose
myStopping <- StoppingPatientsNearDose(nPatients = 9,
                                       percentage = 0.2)

# Evaluate if to stop the trial
stopTrial(stopping=myStopping, 
          dose=doseRecommendation$value,
          data=data)


# Create the data
data <- Data(x=c(0.1, 0.5, 1.5, 3, 6, 10, 10, 10),
             y=c(0, 0, 0, 0, 0, 0, 1, 0),
             cohort=c(0, 1, 2, 3, 4, 5, 5, 5),
             doseGrid=
               c(0.1, 0.5, 1.5, 3, 6,
                 seq(from=10, to=80, by=2)))

# Initialize the CRM model used to model the data
model <- LogisticLogNormal(mean=c(-0.85, 1),
                           cov=
                             matrix(c(1, -0.5, -0.5, 1),
                                    nrow=2),
                           refDose=56)

# Set-up some MCMC parameters and generate samples from the posterior
options <- McmcOptions(burnin=100,
                       step=2,
                       samples=2000)
set.seed(94)
samples <- mcmc(data, model, options)

# Define the rule for dose increments and calculate the maximum dose allowed
myIncrements <- IncrementsRelative(intervals=c(0, 20),
                                   increments=c(1, 0.33))
nextMaxDose <- maxDose(myIncrements,
                       data=data)

# Define the rule which will be used to select the next best dose
# based on the class 'NextBestNCRM'
myNextBest <- NextBestNCRM(target=c(0.2, 0.35),
                           overdose=c(0.35, 1),
                           maxOverdoseProb=0.25)

# Calculate the next best dose
doseRecommendation <- nextBest(myNextBest,
                               doselimit=nextMaxDose,
                               samples=samples, model=model, data=data)

# Define the stopping rule such that the study would be stopped if at least 6 
# cohorts were already dosed 
myStopping <- StoppingMinCohorts(nCohorts = 6)

# Evaluate if to stop the trial
stopTrial(stopping=myStopping, 
          dose=doseRecommendation$value,
          data=data)


# Create the data
data <- Data(x=c(0.1, 0.5, 1.5, 3, 6, 10, 10, 10),
             y=c(0, 0, 0, 0, 0, 0, 1, 0),
             cohort=c(0, 1, 2, 3, 4, 5, 5, 5),
             doseGrid=
               c(0.1, 0.5, 1.5, 3, 6,
                 seq(from=10, to=80, by=2)))

# Initialize the CRM model used to model the data
model <- LogisticLogNormal(mean=c(-0.85, 1),
                           cov=
                             matrix(c(1, -0.5, -0.5, 1),
                                    nrow=2),
                           refDose=56)

# Set-up some MCMC parameters and generate samples from the posterior
options <- McmcOptions(burnin=100,
                       step=2,
                       samples=2000)
set.seed(94)
samples <- mcmc(data, model, options)

# Define the rule for dose increments and calculate the maximum dose allowed
myIncrements <- IncrementsRelative(intervals=c(0, 20),
                                   increments=c(1, 0.33))
nextMaxDose <- maxDose(myIncrements,
                       data=data)

# Define the rule which will be used to select the next best dose
# based on the class 'NextBestNCRM'
myNextBest <- NextBestNCRM(target=c(0.2, 0.35),
                           overdose=c(0.35, 1),
                           maxOverdoseProb=0.25)

# Calculate the next best dose
doseRecommendation <- nextBest(myNextBest,
                               doselimit=nextMaxDose,
                               samples=samples, model=model, data=data)

# Define the stopping rule such that the study would be stopped if at least 20 
# patients were already dosed 
myStopping <- StoppingMinPatients(nPatients = 20)

# Evaluate if to stop the trial
stopTrial(stopping=myStopping, 
          dose=doseRecommendation$value,
          data=data)


# Create the data
data <- Data(x=c(0.1, 0.5, 1.5, 3, 6, 10, 10, 10),
             y=c(0, 0, 0, 0, 0, 0, 1, 0),
             cohort=c(0, 1, 2, 3, 4, 5, 5, 5),
             doseGrid=
               c(0.1, 0.5, 1.5, 3, 6,
                 seq(from=10, to=80, by=2)))

# Initialize the CRM model used to model the data
model <- LogisticLogNormal(mean=c(-0.85, 1),
                           cov=
                             matrix(c(1, -0.5, -0.5, 1),
                                    nrow=2),
                           refDose=56)

# Set-up some MCMC parameters and generate samples from the posterior
options <- McmcOptions(burnin=100,
                       step=2,
                       samples=2000)
set.seed(94)
samples <- mcmc(data, model, options)

# Define the rule for dose increments and calculate the maximum dose allowed
myIncrements <- IncrementsRelative(intervals=c(0, 20),
                                   increments=c(1, 0.33))
nextMaxDose <- maxDose(myIncrements,
                       data=data)

# Define the rule which will be used to select the next best dose
# based on the class 'NextBestNCRM'
myNextBest <- NextBestNCRM(target=c(0.2, 0.35),
                           overdose=c(0.35, 1),
                           maxOverdoseProb=0.25)

# Calculate the next best dose
doseRecommendation <- nextBest(myNextBest,
                               doselimit=nextMaxDose,
                               samples=samples, model=model, data=data)

# Define the stopping rule such that the study would be stopped if there is at least 
# 0.5 posterior probability that [0.2 =< Prob(DLT | next-best-dose) <= 0.35] 
myStopping <- StoppingTargetProb(target=c(0.2, 0.35),
                                 prob=0.5)

# Evaluate if to stop the trial
stopTrial(stopping=myStopping, 
          dose=doseRecommendation$value,
          samples=samples,
          model=model,
          data=data)


# Create the data
data <- Data(x=c(0.1, 0.5, 1.5, 3, 6, 10, 10, 10),
             y=c(0, 0, 0, 0, 0, 0, 1, 0),
             cohort=c(0, 1, 2, 3, 4, 5, 5, 5),
             doseGrid=
               c(0.1, 0.5, 1.5, 3, 6,
                 seq(from=10, to=80, by=2)))

# Initialize the CRM model used to model the data
model <- LogisticLogNormal(mean=c(-0.85, 1),
                           cov=
                             matrix(c(1, -0.5, -0.5, 1),
                                    nrow=2),
                           refDose=56)

# Set-up some MCMC parameters and generate samples from the posterior
options <- McmcOptions(burnin=100,
                       step=2,
                       samples=2000)
set.seed(94)
samples <- mcmc(data, model, options)

# Define the rule for dose increments and calculate the maximum dose allowed
myIncrements <- IncrementsRelative(intervals=c(0, 20),
                                   increments=c(1, 0.33))
nextMaxDose <- maxDose(myIncrements,
                       data=data)

# Define the rule which will be used to select the next best dose
# based on the class 'NextBestNCRM'
myNextBest <- NextBestNCRM(target=c(0.2, 0.35),
                           overdose=c(0.35, 1),
                           maxOverdoseProb=0.25)

# Calculate the next best dose
doseRecommendation <- nextBest(myNextBest,
                               doselimit=nextMaxDose,
                               samples=samples, model=model, data=data)

# Define the stopping rule such that the study would be stopped if there is at least 
# 0.9 probability that MTD > 0.5*next_best_dose. Here MTD is defined as the dose for 
# which prob(DLE)=0.33
myStopping <- StoppingMTDdistribution(target = 0.33,
                                      thresh = 0.5,
                                      prob = 0.9)

# Evaluate if to stop the trial
stopTrial(stopping=myStopping, 
          dose=doseRecommendation$value,
          samples=samples,
          model=model,
          data=data)


# Create the data
data <- DataDual(
  x=c(0.1, 0.5, 1.5, 3, 6, 10, 10, 10,
      20, 20, 20, 40, 40, 40, 50, 50, 50),
  y=c(0, 0, 0, 0, 0, 0, 1, 0,
      0, 1, 1, 0, 0, 1, 0, 1, 1),
  w=c(0.31, 0.42, 0.59, 0.45, 0.6, 0.7, 0.55, 0.6,
      0.52, 0.54, 0.56, 0.43, 0.41, 0.39, 0.34, 0.38, 0.21),
  doseGrid=c(0.1, 0.5, 1.5, 3, 6,
             seq(from=10, to=80, by=2)))

# Initialize the Dual-Endpoint model (in this case RW1)
model <- DualEndpointRW(mu = c(0, 1),
                        Sigma = matrix(c(1, 0, 0, 1), nrow=2),
                        sigma2betaW = 0.01,
                        sigma2W = c(a=0.1, b=0.1),
                        rho = c(a=1, b=1),
                        smooth = "RW1")

# Set-up some MCMC parameters and generate samples from the posterior
options <- McmcOptions(burnin=100,
                       step=2,
                       samples=500)
set.seed(94)
samples <- mcmc(data, model, options)

# Define the rule for dose increments and calculate the maximum dose allowed
myIncrements <- IncrementsRelative(intervals=c(0, 20),
                                   increments=c(1, 0.33))
nextMaxDose <- maxDose(myIncrements,
                       data=data)

# Define the rule which will be used to select the next best dose
# In this case target a dose achieving at least 0.9 of maximum biomarker level (efficacy)
# and with a probability below 0.25 that prob(DLT)>0.35 (safety)
myNextBest <- NextBestDualEndpoint(target=c(0.9, 1),
                                   overdose=c(0.35, 1),
                                   maxOverdoseProb=0.25)

# Calculate the next best dose
doseRecommendation <- nextBest(myNextBest,
                               doselimit=nextMaxDose,
                               samples=samples,
                               model=model,
                               data=data)

# Define the stopping rule such that the study would be stopped if if there is at 
# least 0.5 posterior probability that the biomarker (efficacy) is within the 
# biomarker target range of [0.9, 1.0] (relative to the maximum for the biomarker). 
myStopping <- StoppingTargetBiomarker(target = c(0.9, 1),
                                      prob = 0.5)

# Evaluate if to stop the trial
stopTrial(stopping=myStopping, 
          dose=doseRecommendation$value,
          samples=samples,
          model=model,
          data=data)

# Create the data
data <- Data(x=c(0.1, 0.5, 1.5, 3, 6, 10, 10, 10, 20, 20, 20, 40, 40, 40,
                 80, 80, 80),
             y=c(0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0),
             cohort=c(0, 1, 2, 3, 4, 5, 5, 5, 6, 6, 6, 7, 7, 7, 8, 8, 8),
             doseGrid=
               c(0.1, 0.5, 1.5, 3, 6,
                 seq(from=10, to=80, by=2)))

# Initialize the CRM model used to model the data
model <- LogisticLogNormal(mean=c(-0.85, 1),
                           cov=
                             matrix(c(1, -0.5, -0.5, 1),
                                    nrow=2),
                           refDose=56)

# Set-up some MCMC parameters and generate samples from the posterior
options <- McmcOptions(burnin=100,
                       step=2,
                       samples=2000)
set.seed(94)
samples <- mcmc(data, model, options)

# Define the rule for dose increments and calculate the maximum dose allowed
myIncrements <- IncrementsRelative(intervals=c(0, 20),
                                   increments=c(1, 0.33))
nextMaxDose <- maxDose(myIncrements,
                       data=data)

# Define the rule which will be used to select the next best dose
# based on the class 'NextBestNCRM'
myNextBest <- NextBestNCRM(target=c(0.2, 0.35),
                           overdose=c(0.35, 1),
                           maxOverdoseProb=0.25)

# Calculate the next best dose
doseRecommendation <- nextBest(myNextBest,
                               doselimit=nextMaxDose,
                               samples=samples, model=model, data=data)

# Define the stopping rule such that the study would be stopped if there is at least 
# 0.5 posterior probability that [0.2 =< Prob(DLT | next-best-dose) <= 0.35] 
stopTarget <- StoppingTargetProb(target=c(0.2, 0.35),
                                 prob=0.5)

## now use the StoppingHighestDose rule:
stopHigh <-
  StoppingHighestDose() &
  StoppingPatientsNearDose(nPatients=3, percentage=0) &
  StoppingTargetProb(target=c(0, 0.2),
                     prob=0.5)

## and combine everything:
myStopping <- stopTarget | stopHigh

# Then evaluate if to stop the trial
stopTrial(stopping=myStopping, 
          dose=doseRecommendation$value,
          samples=samples,
          model=model,
          data=data)



##define the stopping rules based on the 'StoppingTDCIRatio' class
##Using only DLE responses with samples
## we need a data object with doses >= 1:
data<-Data(x=c(25,50,50,75,150,200,225,300),
           y=c(0,0,0,0,1,1,1,1),
           doseGrid=seq(from=25,to=300,by=25))

##model can be specified of 'Model' or 'ModelTox' class
##For example, the 'logisticIndepBeta' class model
model<-LogisticIndepBeta(binDLE=c(1.05,1.8),DLEweights=c(3,3),DLEdose=c(25,300),data=data)
##define MCMC options
##for illustration purpose we use 10 burn-in and generate 50 samples
options<-McmcOptions(burnin=10,step=2,samples=50)
##samples of 'Samples' class
samples<-mcmc(data,model,options)
##define the 'StoppingTDCIRatio' class
myStopping <- StoppingTDCIRatio(targetRatio=5,
                                       targetEndOfTrial=0.3)
##Find the next Recommend dose using the nextBest method (plesae refer to nextbest examples)
tdNextBest<-NextBestTDsamples(targetDuringTrial=0.35,targetEndOfTrial=0.3,
                              derive=function(TDsamples){quantile(TDsamples,probs=0.3)})


RecommendDose<-nextBest(tdNextBest,doselimit=max(data@doseGrid),samples=samples,
                        model=model,data=data)
##use 'stopTrial' to determine if the rule has been fulfilled
##use 0.3 as the target proability of DLE at the end of the trial

stopTrial(stopping=myStopping,dose=RecommendDose$nextdose,
          samples=samples,model=model,data=data)
## RecommendDose$nextdose refers to the next dose obtained in RecommendDose
##define the stopping rules based on the 'StoppingTDCIRatio' class
##Using only DLE responses 
## we need a data object with doses >= 1:
data<-Data(x=c(25,50,50,75,150,200,225,300),
           y=c(0,0,0,0,1,1,1,1),
           doseGrid=seq(from=25,to=300,by=25))

##model must be of 'ModelTox' class
##For example, the 'logisticIndepBeta' class model
model<-LogisticIndepBeta(binDLE=c(1.05,1.8),DLEweights=c(3,3),DLEdose=c(25,300),data=data)
##define the 'StoppingTDCIRatio' class
myStopping <- StoppingTDCIRatio(targetRatio=5,
                                targetEndOfTrial=0.3)
##Find the next Recommend dose using the nextBest method (plesae refer to nextbest examples)
tdNextBest<-NextBestTD(targetDuringTrial=0.35,targetEndOfTrial=0.3)


RecommendDose<-nextBest(tdNextBest,doselimit=max(data@doseGrid),model=model,data=data)
##use 'stopTrial' to determine if the rule has been fulfilled
##use 0.3 as the target proability of DLE at the end of the trial

stopTrial(stopping=myStopping,dose=RecommendDose$nextdose,
          model=model,data=data)
## RecommendDose$nextdose refers to the next dose obtained in RecommendDose
##define the stopping rules based on the 'StoppingGstarCIRatio' class
##Using both DLE and efficacy responses
## we need a data object with doses >= 1:
data <-DataDual(x=c(25,50,25,50,75,300,250,150),
                y=c(0,0,0,0,0,1,1,0),
                w=c(0.31,0.42,0.59,0.45,0.6,0.7,0.6,0.52),
                doseGrid=seq(25,300,25),
                placebo=FALSE)

##DLEmodel must be of 'ModelTox' class
##For example, the 'logisticIndepBeta' class model
DLEmodel<-LogisticIndepBeta(binDLE=c(1.05,1.8),DLEweights=c(3,3),DLEdose=c(25,300),data=data)

##Effmodel must be  of 'ModelEff' class
##For example, the 'Effloglog' class model
Effmodel<-Effloglog(Eff=c(1.223,2.513),Effdose=c(25,300),nu=c(a=1,b=0.025),data=data,c=0)
##for illustration purpose we use 10 burn-in and generate 50 samples
options<-McmcOptions(burnin=10,step=2,samples=50)
##DLE and efficacy samples must be of 'Samples' class
DLEsamples<-mcmc(data,DLEmodel,options)
Effsamples<-mcmc(data,Effmodel,options)

##define the 'StoppingGstarCIRatio' class
myStopping <- StoppingGstarCIRatio(targetRatio=5,
                                          targetEndOfTrial=0.3)
##Find the next Recommend dose using the nextBest method (plesae refer to nextbest examples)
mynextbest<-NextBestMaxGainSamples(DLEDuringTrialtarget=0.35,
                                   DLEEndOfTrialtarget=0.3,
                                   TDderive=function(TDsamples){
                                     quantile(TDsamples,prob=0.3)},
                                   Gstarderive=function(Gstarsamples){
                                     quantile(Gstarsamples,prob=0.5)})

RecommendDose<-nextBest(mynextbest,doselimit=max(data@doseGrid),samples=DLEsamples,model=DLEmodel,
                        data=data,Effmodel=Effmodel,Effsamples=Effsamples)
##use 'stopTrial' to determine if the rule has been fulfilled
##use 0.3 as the target proability of DLE at the end of the trial 

stopTrial(stopping=myStopping,
          dose=RecommendDose$nextdose,
          samples=DLEsamples,
          model=DLEmodel,
          data=data,
          TDderive=function(TDsamples){
            quantile(TDsamples,prob=0.3)},
          Effmodel=Effmodel,
          Effsamples=Effsamples,
          Gstarderive=function(Gstarsamples){
            quantile(Gstarsamples,prob=0.5)})

## RecommendDose$nextdose refers to the next dose obtained in RecommendDose
##define the stopping rules based on the 'StoppingGstarCIRatio' class
##Using both DLE and efficacy responses
## we need a data object with doses >= 1:
data <-DataDual(x=c(25,50,25,50,75,300,250,150),
                y=c(0,0,0,0,0,1,1,0),
                w=c(0.31,0.42,0.59,0.45,0.6,0.7,0.6,0.52),
                doseGrid=seq(25,300,25),
                placebo=FALSE)

##DLEmodel must be of 'ModelTox' class
##For example, the 'logisticIndepBeta' class model
DLEmodel<-LogisticIndepBeta(binDLE=c(1.05,1.8),DLEweights=c(3,3),DLEdose=c(25,300),data=data)

##Effmodel must be  of 'ModelEff' class
##For example, the 'Effloglog' class model
Effmodel<-Effloglog(Eff=c(1.223,2.513),Effdose=c(25,300),nu=c(a=1,b=0.025),data=data,c=0)


##define the 'StoppingGstarCIRatio' class
myStopping <- StoppingGstarCIRatio(targetRatio=5,
                                   targetEndOfTrial=0.3)
##Find the next Recommend dose using the nextBest method (plesae refer to nextbest examples)
mynextbest<-NextBestMaxGain(DLEDuringTrialtarget=0.35,DLEEndOfTrialtarget=0.3)

RecommendDose<-nextBest(mynextbest,doselimit=max(data@doseGrid),model=DLEmodel,
                        Effmodel=Effmodel,data=data)

##use 'stopTrial' to determine if the rule has been fulfilled
##use 0.3 as the target proability of DLE at the end of the trial


stopTrial(stopping=myStopping,dose=RecommendDose$nextdose,model=DLEmodel,
          data=data, Effmodel=Effmodel)


## RecommendDose$nextdose refers to the next dose obtained in RecommendDose

Summarize the dual-endpoint design simulations, relative to given true dose-toxicity and dose-biomarker curves

Description

Summarize the dual-endpoint design simulations, relative to given true dose-toxicity and dose-biomarker curves

Usage

## S4 method for signature 'DualSimulations'
summary(object, trueTox, trueBiomarker, target = c(0.2, 0.35), ...)
## S4 method for signature 'DualSimulations'
summary(object, trueTox, trueBiomarker, target = c(0.2, 0.35), ...)

Arguments

`object`	the `DualSimulations` object we want to summarize
`trueTox`	a function which takes as input a dose (vector) and returns the true probability (vector) for toxicity.
`trueBiomarker`	a function which takes as input a dose (vector) and returns the true biomarker level (vector).
`target`	the target toxicity interval (default: 20-35%) used for the computations
`...`	Additional arguments can be supplied here for `trueTox` and `trueBiomarker`

Value

an object of class DualSimulationsSummary

Examples


# Define the dose-grid
emptydata <- DataDual(doseGrid = c(1, 3, 5, 10, 15, 20, 25, 40, 50, 80, 100))

# Initialize the CRM model 
model <- DualEndpointRW(mu = c(0, 1),
                        Sigma = matrix(c(1, 0, 0, 1), nrow=2),
                        sigma2betaW = 0.01,
                        sigma2W = c(a=0.1, b=0.1),
                        rho = c(a=1, b=1),
                        smooth="RW1")

# Choose the rule for selecting the next dose 
myNextBest <- NextBestDualEndpoint(target=c(0.9, 1),
                                   overdose=c(0.35, 1),
                                   maxOverdoseProb=0.25)

# Choose the rule for stopping
myStopping4 <- StoppingTargetBiomarker(target=c(0.9, 1),
                                       prob=0.5)
# StoppingMinPatients will usually take a higher sample size,
# just for illustration here
myStopping <- myStopping4 | StoppingMinPatients(6)

# Choose the rule for dose increments
myIncrements <- IncrementsRelative(intervals=c(0, 20),
                                   increments=c(1, 0.33))

# Initialize the design
design <- DualDesign(model = model,
                     data = emptydata,
                     nextBest = myNextBest,
                     stopping = myStopping,
                     increments = myIncrements,
                     cohortSize = CohortSizeConst(3),
                     startingDose = 3)
  
# define scenarios for the TRUE toxicity and efficacy profiles
betaMod <- function (dose, e0, eMax, delta1, delta2, scal)
{
  maxDens <- (delta1^delta1) * (delta2^delta2)/((delta1 + delta2)^(delta1 + delta2))
  dose <- dose/scal
  e0 + eMax/maxDens * (dose^delta1) * (1 - dose)^delta2
}

trueBiomarker <- function(dose)
{
  betaMod(dose, e0=0.2, eMax=0.6, delta1=5, delta2=5 * 0.5 / 0.5, scal=100)
}

trueTox <- function(dose)
{
  pnorm((dose-60)/10)
}

# Draw the TRUE profiles
par(mfrow=c(1, 2))
curve(trueTox(x), from=0, to=80)
curve(trueBiomarker(x), from=0, to=80)

# Run the simulation on the desired design
# We only generate 1 trial outcome here for illustration, for the actual study
# Also for illustration purpose, we will use 5 burn-ins to generate 20 samples
# this should be increased of course!
mySims <- simulate(design,
                   trueTox=trueTox,
                   trueBiomarker=trueBiomarker,
                   sigma2W=0.01,
                   rho=0,
                   nsim=1,
                   parallel=FALSE,
                   seed=3,
                   startingDose=6,
                   mcmcOptions =
                     McmcOptions(burnin=5,
                                 step=1,
                                 samples=20))

# Summarize the Results of the Simulations
summary(mySims,
        trueTox = trueTox,
        trueBiomarker = trueBiomarker)



  
# Define the dose-grid
emptydata <- DataDual(doseGrid = c(1, 3, 5, 10, 15, 20, 25, 40, 50, 80, 100))

# Initialize the CRM model 
model <- DualEndpointRW(mu = c(0, 1),
                        Sigma = matrix(c(1, 0, 0, 1), nrow=2),
                        sigma2betaW = 0.01,
                        sigma2W = c(a=0.1, b=0.1),
                        rho = c(a=1, b=1),
                        smooth="RW1")

# Choose the rule for selecting the next dose 
myNextBest <- NextBestDualEndpoint(target=c(0.9, 1),
                                   overdose=c(0.35, 1),
                                   maxOverdoseProb=0.25)

# Choose the rule for stopping
myStopping4 <- StoppingTargetBiomarker(target=c(0.9, 1),
                                       prob=0.5)
# StoppingMinPatients will usually take a higher sample size,
# just for illustration here
myStopping <- myStopping4 | StoppingMinPatients(6)

# Choose the rule for dose increments
myIncrements <- IncrementsRelative(intervals=c(0, 20),
                                   increments=c(1, 0.33))

# Initialize the design
design <- DualDesign(model = model,
                     data = emptydata,
                     nextBest = myNextBest,
                     stopping = myStopping,
                     increments = myIncrements,
                     cohortSize = CohortSizeConst(3),
                     startingDose = 3)
  
# define scenarios for the TRUE toxicity and efficacy profiles
betaMod <- function (dose, e0, eMax, delta1, delta2, scal)
{
  maxDens <- (delta1^delta1) * (delta2^delta2)/((delta1 + delta2)^(delta1 + delta2))
  dose <- dose/scal
  e0 + eMax/maxDens * (dose^delta1) * (1 - dose)^delta2
}

trueBiomarker <- function(dose)
{
  betaMod(dose, e0=0.2, eMax=0.6, delta1=5, delta2=5 * 0.5 / 0.5, scal=100)
}

trueTox <- function(dose)
{
  pnorm((dose-60)/10)
}

# Draw the TRUE profiles
par(mfrow=c(1, 2))
curve(trueTox(x), from=0, to=80)
curve(trueBiomarker(x), from=0, to=80)

# Run the simulation on the desired design
# We only generate 1 trial outcome here for illustration, for the actual study
# Also for illustration purpose, we will use 5 burn-ins to generate 20 samples
# this should be increased of course!
mySims <- simulate(design,
                   trueTox=trueTox,
                   trueBiomarker=trueBiomarker,
                   sigma2W=0.01,
                   rho=0,
                   nsim=1,
                   parallel=FALSE,
                   seed=3,
                   startingDose=6,
                   mcmcOptions =
                     McmcOptions(burnin=5,
                                 step=1,
                                 samples=20))

# Summarize the Results of the Simulations
summary(mySims,
        trueTox = trueTox,
        trueBiomarker = trueBiomarker)

Summarize the simulations, relative to a given truth

Description

Summarize the simulations, relative to a given truth

Usage

## S4 method for signature 'GeneralSimulations'
summary(object, truth, target = c(0.2, 0.35), ...)
## S4 method for signature 'GeneralSimulations'
summary(object, truth, target = c(0.2, 0.35), ...)

Arguments

`object`	the `GeneralSimulations` object we want to summarize
`truth`	a function which takes as input a dose (vector) and returns the true probability (vector) for toxicity
`target`	the target toxicity interval (default: 20-35%) used for the computations
`...`	Additional arguments can be supplied here for `truth`

Value

an object of class GeneralSimulationsSummary

Summary for Pseudo Dual responses simulations given a pseudo DLE model and the Flexible efficacy model.

Description

Summary for Pseudo Dual responses simulations given a pseudo DLE model and the Flexible efficacy model.

Usage

## S4 method for signature 'PseudoDualFlexiSimulations'
summary(
  object,
  trueDLE,
  trueEff,
  targetEndOfTrial = 0.3,
  targetDuringTrial = 0.35,
  ...
)
## S4 method for signature 'PseudoDualFlexiSimulations'
summary(
  object,
  trueDLE,
  trueEff,
  targetEndOfTrial = 0.3,
  targetDuringTrial = 0.35,
  ...
)

Arguments

`object`	the `PseudoDualFlexiSimulations` object we want to summarize
`trueDLE`	a function which takes as input a dose (vector) and returns the true probability of DLE (vector)
`trueEff`	a vector which takes as input the true mean efficacy values at all dose levels (in order)
`targetEndOfTrial`	the target probability of DLE that are used at the end of a trial. Default at 0.3.
`targetDuringTrial`	the target probability of DLE that are used during the trial. Default at 0.35.
`...`	Additional arguments can be supplied here for `trueDLE` and `trueEff`

Value

an object of class PseudoDualSimulationsSummary

Examples


##If DLE and efficacy responses are considered in the simulations and the 'EffFlexi' class is used 
## we need a data object with doses >= 1:
data <- DataDual(doseGrid=seq(25,300,25))
##First for the DLE model 
##The DLE model must be of 'ModelTox' (e.g 'LogisticIndepBeta') class 
DLEmodel <- LogisticIndepBeta(binDLE=c(1.05,1.8),
                              DLEweights=c(3,3),
                              DLEdose=c(25,300),
                              data=data)

## for the efficacy model
Effmodel<- EffFlexi(Eff=c(1.223, 2.513),Effdose=c(25,300),
                    sigma2=c(a=0.1,b=0.1),sigma2betaW=c(a=20,b=50),smooth="RW2",data=data)


##specified the next best
mynextbest<-NextBestMaxGainSamples(DLEDuringTrialtarget=0.35,
                                   DLEEndOfTrialtarget=0.3,
                                   TDderive=function(TDsamples){
                                     quantile(TDsamples,prob=0.3)},
                                   Gstarderive=function(Gstarsamples){
                                     quantile(Gstarsamples,prob=0.5)})

##The increments (see Increments class examples) 
## 200% allowable increase for dose below 300 and 200% increase for dose above 300
myIncrements<-IncrementsRelative(intervals=c(25,300),
                                 increments=c(2,2))
##cohort size of 3
mySize<-CohortSizeConst(size=3)
##Stop only when 10 subjects are treated:
## very low sample size is just for illustration here
myStopping <- StoppingMinPatients(nPatients=10)

##Specified the design 
design <- DualResponsesSamplesDesign(nextBest=mynextbest,
                                     cohortSize=mySize,
                                     startingDose=25,
                                     model=DLEmodel,
                                     Effmodel=Effmodel,
                                     data=data,
                                     stopping=myStopping,
                                     increments=myIncrements)
##specified the true DLE curve and the true expected efficacy values at all dose levels
myTruthDLE<- function(dose)
{ DLEmodel@prob(dose, phi1=-53.66584, phi2=10.50499)
}

myTruthEff<- c(-0.5478867, 0.1645417,  0.5248031,  0.7604467,  
               0.9333009  ,1.0687031,  1.1793942 , 1.2726408 , 
               1.3529598 , 1.4233411 , 1.4858613 , 1.5420182)


##specify the options for MCMC
#For illustration purpose, we use 10 burn-in and generate 100 samples
options<-McmcOptions(burnin=10,step=1,samples=100)
##The simulation
##For illustration purpose only 1 simulation is produced (nsim=1). 
mySim<-simulate(object=design,
                args=NULL,
                trueDLE=myTruthDLE,
                trueEff=myTruthEff,
                trueSigma2=0.025,
                trueSigma2betaW=1,
                nsim=1,
                seed=819,
                parallel=FALSE,
                mcmcOptions=options)
##summarize the simulation results
summary(mySim,
        trueDLE=myTruthDLE,
        trueEff=myTruthEff)
##If DLE and efficacy responses are considered in the simulations and the 'EffFlexi' class is used 
## we need a data object with doses >= 1:
data <- DataDual(doseGrid=seq(25,300,25))
##First for the DLE model 
##The DLE model must be of 'ModelTox' (e.g 'LogisticIndepBeta') class 
DLEmodel <- LogisticIndepBeta(binDLE=c(1.05,1.8),
                              DLEweights=c(3,3),
                              DLEdose=c(25,300),
                              data=data)

## for the efficacy model
Effmodel<- EffFlexi(Eff=c(1.223, 2.513),Effdose=c(25,300),
                    sigma2=c(a=0.1,b=0.1),sigma2betaW=c(a=20,b=50),smooth="RW2",data=data)


##specified the next best
mynextbest<-NextBestMaxGainSamples(DLEDuringTrialtarget=0.35,
                                   DLEEndOfTrialtarget=0.3,
                                   TDderive=function(TDsamples){
                                     quantile(TDsamples,prob=0.3)},
                                   Gstarderive=function(Gstarsamples){
                                     quantile(Gstarsamples,prob=0.5)})

##The increments (see Increments class examples) 
## 200% allowable increase for dose below 300 and 200% increase for dose above 300
myIncrements<-IncrementsRelative(intervals=c(25,300),
                                 increments=c(2,2))
##cohort size of 3
mySize<-CohortSizeConst(size=3)
##Stop only when 10 subjects are treated:
## very low sample size is just for illustration here
myStopping <- StoppingMinPatients(nPatients=10)

##Specified the design 
design <- DualResponsesSamplesDesign(nextBest=mynextbest,
                                     cohortSize=mySize,
                                     startingDose=25,
                                     model=DLEmodel,
                                     Effmodel=Effmodel,
                                     data=data,
                                     stopping=myStopping,
                                     increments=myIncrements)
##specified the true DLE curve and the true expected efficacy values at all dose levels
myTruthDLE<- function(dose)
{ DLEmodel@prob(dose, phi1=-53.66584, phi2=10.50499)
}

myTruthEff<- c(-0.5478867, 0.1645417,  0.5248031,  0.7604467,  
               0.9333009  ,1.0687031,  1.1793942 , 1.2726408 , 
               1.3529598 , 1.4233411 , 1.4858613 , 1.5420182)


##specify the options for MCMC
#For illustration purpose, we use 10 burn-in and generate 100 samples
options<-McmcOptions(burnin=10,step=1,samples=100)
##The simulation
##For illustration purpose only 1 simulation is produced (nsim=1). 
mySim<-simulate(object=design,
                args=NULL,
                trueDLE=myTruthDLE,
                trueEff=myTruthEff,
                trueSigma2=0.025,
                trueSigma2betaW=1,
                nsim=1,
                seed=819,
                parallel=FALSE,
                mcmcOptions=options)
##summarize the simulation results
summary(mySim,
        trueDLE=myTruthDLE,
        trueEff=myTruthEff)

Summary for Pseudo Dual responses simulations, relative to a given pseudo DLE and efficacy model (except the EffFlexi class model)

Description

Summary for Pseudo Dual responses simulations, relative to a given pseudo DLE and efficacy model (except the EffFlexi class model)

Usage

## S4 method for signature 'PseudoDualSimulations'
summary(
  object,
  trueDLE,
  trueEff,
  targetEndOfTrial = 0.3,
  targetDuringTrial = 0.35,
  ...
)
## S4 method for signature 'PseudoDualSimulations'
summary(
  object,
  trueDLE,
  trueEff,
  targetEndOfTrial = 0.3,
  targetDuringTrial = 0.35,
  ...
)

Arguments

`object`	the `PseudoDualSimulations` object we want to summarize
`trueDLE`	a function which takes as input a dose (vector) and returns the true probability (vector) of DLE
`trueEff`	a function which takes as input a dose (vector) and returns the mean efficacy value(s) (vector).
`targetEndOfTrial`	the target probability of DLE that are used at the end of a trial. Default at 0.3.
`targetDuringTrial`	the target probability of DLE that are used during the trial. Default at 0.35.
`...`	Additional arguments can be supplied here for `trueDLE` and `trueEff`

Value

an object of class PseudoDualSimulationsSummary

Examples

##obtain the plot for the simulation results
##If DLE and efficacy responses are considered in the simulations
##Specified your simulations when no samples are used
data <- DataDual(doseGrid=seq(25,300,25))
##First for the DLE model 
##The DLE model must be of 'ModelTox' (e.g 'LogisticIndepBeta') class 
DLEmodel <- LogisticIndepBeta(binDLE=c(1.05,1.8),
                              DLEweights=c(3,3),
                              DLEdose=c(25,300),
                              data=data)

##The efficacy model of 'ModelEff' (e.g 'Effloglog') class 
Effmodel<-Effloglog(Eff=c(1.223,2.513),Effdose=c(25,300),
                    nu=c(a=1,b=0.025),data=data)

##The escalation rule using the 'NextBestMaxGain' class
mynextbest<-NextBestMaxGain(DLEDuringTrialtarget=0.35,
                            DLEEndOfTrialtarget=0.3)


##The increments (see Increments class examples) 
## 200% allowable increase for dose below 300 and 200% increase for dose above 300
myIncrements<-IncrementsRelative(intervals=c(25,300),
                                 increments=c(2,2))
##cohort size of 3
mySize<-CohortSizeConst(size=3)
##Stop only when 36 subjects are treated
myStopping <- StoppingMinPatients(nPatients=36)
##Now specified the design with all the above information and starting with a dose of 25

##Specified the design(for details please refer to the 'DualResponsesDesign' example)
design <- DualResponsesDesign(nextBest=mynextbest,
                              model=DLEmodel,
                              Effmodel=Effmodel,
                              stopping=myStopping,
                              increments=myIncrements,
                              cohortSize=mySize,
                              data=data,startingDose=25)
##Specify the true DLE and efficacy curves
myTruthDLE<- function(dose)
{ DLEmodel@prob(dose, phi1=-53.66584, phi2=10.50499)
}

myTruthEff<- function(dose)
{Effmodel@ExpEff(dose,theta1=-4.818429,theta2=3.653058)
}

## Then specified the simulations and generate the trial for 2 times
mySim <-simulate(object=design,
                 args=NULL,
                 trueDLE=myTruthDLE,
                 trueEff=myTruthEff,
                 trueNu=1/0.025,
                 nsim=2,
                 seed=819,
                 parallel=FALSE)

##Then produce a summary of your simulations
summary(mySim,
        trueDLE=myTruthDLE,
        trueEff=myTruthEff)
##If DLE and efficacy samples are involved
##Please refer to design-method 'simulate DualResponsesSamplesDesign' examples for details
##specified the next best
mynextbest<-NextBestMaxGainSamples(DLEDuringTrialtarget=0.35,
                                   DLEEndOfTrialtarget=0.3,
                                   TDderive=function(TDsamples){
                                     quantile(TDsamples,prob=0.3)},
                                   Gstarderive=function(Gstarsamples){
                                     quantile(Gstarsamples,prob=0.5)})
##specified the design
design <- DualResponsesSamplesDesign(nextBest=mynextbest,
                                     cohortSize=mySize,
                                     startingDose=25,
                                     model=DLEmodel,
                                     Effmodel=Effmodel,
                                     data=data,
                                     stopping=myStopping,
                                     increments=myIncrements)
##options for MCMC
##For illustration purpose, we will use 50 burn-ins to generate 200 samples
options<-McmcOptions(burnin=50,step=2,samples=200)
##The simulations
##For illustration purpose only 2 simulation is produced (nsim=2). 
mySim<-simulate(design,
                args=NULL,
                trueDLE=myTruthDLE,
                trueEff=myTruthEff,
                trueNu=1/0.025,
                nsim=2,
                mcmcOptions=options,
                seed=819,
                parallel=FALSE)

##Then produce a summary of your simulations
summary(mySim,
        trueDLE=myTruthDLE,
        trueEff=myTruthEff)

##obtain the plot for the simulation results
##If DLE and efficacy responses are considered in the simulations
##Specified your simulations when no samples are used
data <- DataDual(doseGrid=seq(25,300,25))
##First for the DLE model 
##The DLE model must be of 'ModelTox' (e.g 'LogisticIndepBeta') class 
DLEmodel <- LogisticIndepBeta(binDLE=c(1.05,1.8),
                              DLEweights=c(3,3),
                              DLEdose=c(25,300),
                              data=data)

##The efficacy model of 'ModelEff' (e.g 'Effloglog') class 
Effmodel<-Effloglog(Eff=c(1.223,2.513),Effdose=c(25,300),
                    nu=c(a=1,b=0.025),data=data)

##The escalation rule using the 'NextBestMaxGain' class
mynextbest<-NextBestMaxGain(DLEDuringTrialtarget=0.35,
                            DLEEndOfTrialtarget=0.3)


##The increments (see Increments class examples) 
## 200% allowable increase for dose below 300 and 200% increase for dose above 300
myIncrements<-IncrementsRelative(intervals=c(25,300),
                                 increments=c(2,2))
##cohort size of 3
mySize<-CohortSizeConst(size=3)
##Stop only when 36 subjects are treated
myStopping <- StoppingMinPatients(nPatients=36)
##Now specified the design with all the above information and starting with a dose of 25

##Specified the design(for details please refer to the 'DualResponsesDesign' example)
design <- DualResponsesDesign(nextBest=mynextbest,
                              model=DLEmodel,
                              Effmodel=Effmodel,
                              stopping=myStopping,
                              increments=myIncrements,
                              cohortSize=mySize,
                              data=data,startingDose=25)
##Specify the true DLE and efficacy curves
myTruthDLE<- function(dose)
{ DLEmodel@prob(dose, phi1=-53.66584, phi2=10.50499)
}

myTruthEff<- function(dose)
{Effmodel@ExpEff(dose,theta1=-4.818429,theta2=3.653058)
}

## Then specified the simulations and generate the trial for 2 times
mySim <-simulate(object=design,
                 args=NULL,
                 trueDLE=myTruthDLE,
                 trueEff=myTruthEff,
                 trueNu=1/0.025,
                 nsim=2,
                 seed=819,
                 parallel=FALSE)

##Then produce a summary of your simulations
summary(mySim,
        trueDLE=myTruthDLE,
        trueEff=myTruthEff)
##If DLE and efficacy samples are involved
##Please refer to design-method 'simulate DualResponsesSamplesDesign' examples for details
##specified the next best
mynextbest<-NextBestMaxGainSamples(DLEDuringTrialtarget=0.35,
                                   DLEEndOfTrialtarget=0.3,
                                   TDderive=function(TDsamples){
                                     quantile(TDsamples,prob=0.3)},
                                   Gstarderive=function(Gstarsamples){
                                     quantile(Gstarsamples,prob=0.5)})
##specified the design
design <- DualResponsesSamplesDesign(nextBest=mynextbest,
                                     cohortSize=mySize,
                                     startingDose=25,
                                     model=DLEmodel,
                                     Effmodel=Effmodel,
                                     data=data,
                                     stopping=myStopping,
                                     increments=myIncrements)
##options for MCMC
##For illustration purpose, we will use 50 burn-ins to generate 200 samples
options<-McmcOptions(burnin=50,step=2,samples=200)
##The simulations
##For illustration purpose only 2 simulation is produced (nsim=2). 
mySim<-simulate(design,
                args=NULL,
                trueDLE=myTruthDLE,
                trueEff=myTruthEff,
                trueNu=1/0.025,
                nsim=2,
                mcmcOptions=options,
                seed=819,
                parallel=FALSE)

##Then produce a summary of your simulations
summary(mySim,
        trueDLE=myTruthDLE,
        trueEff=myTruthEff)

Summarize the simulations, relative to a given truth

Description

Summarize the simulations, relative to a given truth

Usage

## S4 method for signature 'PseudoSimulations'
summary(object, truth, targetEndOfTrial = 0.3, targetDuringTrial = 0.35, ...)
## S4 method for signature 'PseudoSimulations'
summary(object, truth, targetEndOfTrial = 0.3, targetDuringTrial = 0.35, ...)

Arguments

`object`	the `PseudoSimulations` object we want to summarize
`truth`	a function which takes as input a dose (vector) and returns the true probability (vector) for toxicity
`targetEndOfTrial`	the target probability of DLE wanted to achieve at the end of a trial
`targetDuringTrial`	the target probability of DLE wanted to achieve during a trial
`...`	Additional arguments can be supplied here for `truth`

Value

an object of class PseudoSimulationsSummary

Examples


##If only DLE responses are considered in the simulations
##Specified your simulations when no DLE samples are used
## data set with dose levels from 25 to 300 with increments 25
data <- Data(doseGrid=seq(25,300,25))

##The design only incorporate DLE responses and DLE samples are involved
##Specified the model of 'ModelTox' class eg 'LogisticIndepBeta' class model
model<-LogisticIndepBeta(binDLE=c(1.05,1.8),DLEweights=c(3,3),DLEdose=c(25,300),data=data)
##Then the escalation rule
tdNextBest <- NextBestTD(targetDuringTrial=0.35,
                         targetEndOfTrial=0.3)

##Then the starting data, an empty data set
emptydata<-Data(doseGrid=seq(25,300,25))
## The cohort size, size of 3 subjects
mySize <-CohortSizeConst(size=3)
##Deifne the increments for the dose-escalation process
##The maximum increase of 200% for doses up to the maximum of the dose specified in the doseGrid
##The maximum increase of 200% for dose above the maximum of the dose specified in the doseGrid
##This is to specified a maximum of 3-fold restriction in dose-esclation
myIncrements<-IncrementsRelative(intervals=c(min(data@doseGrid),max(data@doseGrid)), 
                                 increments=c(2,2))
##Specified the stopping rule e.g stop when the maximum sample size of 36 patients has been reached
myStopping <- StoppingMinPatients(nPatients=36)


##Specified the design(for details please refer to the 'TDDesign' example)
design <- TDDesign(model=model,
                   nextBest=tdNextBest,
                   stopping=myStopping,
                   increments=myIncrements,
                   cohortSize=mySize,
                   data=data,startingDose=25)

##Specify the truth of the DLE responses
myTruth <- function(dose)
{ model@prob(dose, phi1=-53.66584, phi2=10.50499)
}



##(Please refer to desgin-method 'simulate TDDesign' examples for details)
##For illustration purpose only 1 simulation is produced (nsim=1). 
mySim <- simulate(design,
                  args=NULL,
                  truth=myTruth,
                  nsim=1,
                  seed=819,
                  parallel=FALSE)
##Then produce a summary of your simulations
summary(mySim,
        truth=myTruth)





##If DLE samples are involved
##specify the next best
tdNextBest<-NextBestTDsamples(targetDuringTrial=0.35,
                              targetEndOfTrial=0.3,
                             derive=function(TDsamples){quantile(TDsamples,probs=0.3)})
##The design
design <- TDsamplesDesign(model=model,
                          nextBest=tdNextBest,
                          stopping=myStopping,
                          increments=myIncrements,
                          cohortSize=mySize,
                          data=data,startingDose=25)
##options for MCMC
##For illustration purpose, we will use 50 burn-ins to generate 200 samples
options<-McmcOptions(burnin=50,step=2,samples=200)
##The simulations
## For illustration purpose we will only generate 2 trials (nsim=2)
mySim <- simulate(design,
                  args=NULL,
                  truth=myTruth,
                  nsim=2,
                  seed=819,
                  mcmcOptions=options,
                  parallel=FALSE)
##Then produce a summary of your simulations
summary(mySim,
        truth=myTruth)
##If only DLE responses are considered in the simulations
##Specified your simulations when no DLE samples are used
## data set with dose levels from 25 to 300 with increments 25
data <- Data(doseGrid=seq(25,300,25))

##The design only incorporate DLE responses and DLE samples are involved
##Specified the model of 'ModelTox' class eg 'LogisticIndepBeta' class model
model<-LogisticIndepBeta(binDLE=c(1.05,1.8),DLEweights=c(3,3),DLEdose=c(25,300),data=data)
##Then the escalation rule
tdNextBest <- NextBestTD(targetDuringTrial=0.35,
                         targetEndOfTrial=0.3)

##Then the starting data, an empty data set
emptydata<-Data(doseGrid=seq(25,300,25))
## The cohort size, size of 3 subjects
mySize <-CohortSizeConst(size=3)
##Deifne the increments for the dose-escalation process
##The maximum increase of 200% for doses up to the maximum of the dose specified in the doseGrid
##The maximum increase of 200% for dose above the maximum of the dose specified in the doseGrid
##This is to specified a maximum of 3-fold restriction in dose-esclation
myIncrements<-IncrementsRelative(intervals=c(min(data@doseGrid),max(data@doseGrid)), 
                                 increments=c(2,2))
##Specified the stopping rule e.g stop when the maximum sample size of 36 patients has been reached
myStopping <- StoppingMinPatients(nPatients=36)


##Specified the design(for details please refer to the 'TDDesign' example)
design <- TDDesign(model=model,
                   nextBest=tdNextBest,
                   stopping=myStopping,
                   increments=myIncrements,
                   cohortSize=mySize,
                   data=data,startingDose=25)

##Specify the truth of the DLE responses
myTruth <- function(dose)
{ model@prob(dose, phi1=-53.66584, phi2=10.50499)
}



##(Please refer to desgin-method 'simulate TDDesign' examples for details)
##For illustration purpose only 1 simulation is produced (nsim=1). 
mySim <- simulate(design,
                  args=NULL,
                  truth=myTruth,
                  nsim=1,
                  seed=819,
                  parallel=FALSE)
##Then produce a summary of your simulations
summary(mySim,
        truth=myTruth)





##If DLE samples are involved
##specify the next best
tdNextBest<-NextBestTDsamples(targetDuringTrial=0.35,
                              targetEndOfTrial=0.3,
                             derive=function(TDsamples){quantile(TDsamples,probs=0.3)})
##The design
design <- TDsamplesDesign(model=model,
                          nextBest=tdNextBest,
                          stopping=myStopping,
                          increments=myIncrements,
                          cohortSize=mySize,
                          data=data,startingDose=25)
##options for MCMC
##For illustration purpose, we will use 50 burn-ins to generate 200 samples
options<-McmcOptions(burnin=50,step=2,samples=200)
##The simulations
## For illustration purpose we will only generate 2 trials (nsim=2)
mySim <- simulate(design,
                  args=NULL,
                  truth=myTruth,
                  nsim=2,
                  seed=819,
                  mcmcOptions=options,
                  parallel=FALSE)
##Then produce a summary of your simulations
summary(mySim,
        truth=myTruth)

Summarize the model-based design simulations, relative to a given truth

Description

Summarize the model-based design simulations, relative to a given truth

Usage

## S4 method for signature 'Simulations'
summary(object, truth, target = c(0.2, 0.35), ...)
## S4 method for signature 'Simulations'
summary(object, truth, target = c(0.2, 0.35), ...)

Arguments

`object`	the `Simulations` object we want to summarize
`truth`	a function which takes as input a dose (vector) and returns the true probability (vector) for toxicity
`target`	the target toxicity interval (default: 20-35%) used for the computations
`...`	Additional arguments can be supplied here for `truth`

Value

an object of class SimulationsSummary

Examples


# Define the dose-grid
emptydata <- Data(doseGrid = c(1, 3, 5, 10, 15, 20, 25, 40, 50, 80, 100))

# Initialize the CRM model 
model <- LogisticLogNormal(mean=c(-0.85, 1),
                           cov=
                             matrix(c(1, -0.5, -0.5, 1),
                                    nrow=2),
                           refDose=56)

# Choose the rule for selecting the next dose 
myNextBest <- NextBestNCRM(target=c(0.2, 0.35),
                           overdose=c(0.35, 1),
                           maxOverdoseProb=0.25)

# Choose the rule for the cohort-size 
mySize1 <- CohortSizeRange(intervals=c(0, 30),
                           cohortSize=c(1, 3))
mySize2 <- CohortSizeDLT(DLTintervals=c(0, 1),
                         cohortSize=c(1, 3))
mySize <- maxSize(mySize1, mySize2)

# Choose the rule for stopping
myStopping1 <- StoppingMinCohorts(nCohorts=3)
myStopping2 <- StoppingTargetProb(target=c(0.2, 0.35),
                                  prob=0.5)
myStopping3 <- StoppingMinPatients(nPatients=20)
myStopping <- (myStopping1 & myStopping2) | myStopping3

# Choose the rule for dose increments
myIncrements <- IncrementsRelative(intervals=c(0, 20),
                                   increments=c(1, 0.33))

# Initialize the design
design <- Design(model=model,
                 nextBest=myNextBest,
                 stopping=myStopping,
                 increments=myIncrements,
                 cohortSize=mySize,
                 data=emptydata,
                 startingDose=3)

## define the true function
myTruth <- function(dose)
{
  model@prob(dose, alpha0=7, alpha1=8)
}

# Run the simulation on the desired design
# We only generate 1 trial outcomes here for illustration, for the actual study 
# this should be increased of course
options <- McmcOptions(burnin=100,
                       step=2,
                       samples=1000)
time <- system.time(mySims <- simulate(design,
                                       args=NULL,
                                       truth=myTruth,
                                       nsim=1,
                                       seed=819,
                                       mcmcOptions=options,
                                       parallel=FALSE))[3]

# Summarize the Results of the Simulations
summary(mySims,truth=myTruth)


# Define the dose-grid
emptydata <- Data(doseGrid = c(1, 3, 5, 10, 15, 20, 25, 40, 50, 80, 100))

# Initialize the CRM model 
model <- LogisticLogNormal(mean=c(-0.85, 1),
                           cov=
                             matrix(c(1, -0.5, -0.5, 1),
                                    nrow=2),
                           refDose=56)

# Choose the rule for selecting the next dose 
myNextBest <- NextBestNCRM(target=c(0.2, 0.35),
                           overdose=c(0.35, 1),
                           maxOverdoseProb=0.25)

# Choose the rule for the cohort-size 
mySize1 <- CohortSizeRange(intervals=c(0, 30),
                           cohortSize=c(1, 3))
mySize2 <- CohortSizeDLT(DLTintervals=c(0, 1),
                         cohortSize=c(1, 3))
mySize <- maxSize(mySize1, mySize2)

# Choose the rule for stopping
myStopping1 <- StoppingMinCohorts(nCohorts=3)
myStopping2 <- StoppingTargetProb(target=c(0.2, 0.35),
                                  prob=0.5)
myStopping3 <- StoppingMinPatients(nPatients=20)
myStopping <- (myStopping1 & myStopping2) | myStopping3

# Choose the rule for dose increments
myIncrements <- IncrementsRelative(intervals=c(0, 20),
                                   increments=c(1, 0.33))

# Initialize the design
design <- Design(model=model,
                 nextBest=myNextBest,
                 stopping=myStopping,
                 increments=myIncrements,
                 cohortSize=mySize,
                 data=emptydata,
                 startingDose=3)

## define the true function
myTruth <- function(dose)
{
  model@prob(dose, alpha0=7, alpha1=8)
}

# Run the simulation on the desired design
# We only generate 1 trial outcomes here for illustration, for the actual study 
# this should be increased of course
options <- McmcOptions(burnin=100,
                       step=2,
                       samples=1000)
time <- system.time(mySims <- simulate(design,
                                       args=NULL,
                                       truth=myTruth,
                                       nsim=1,
                                       seed=819,
                                       mcmcOptions=options,
                                       parallel=FALSE))[3]

# Summarize the Results of the Simulations
summary(mySims,truth=myTruth)

Initialization function for 'TDDesign' class

Description

Initialization function for 'TDDesign' class

Usage

TDDesign(model, stopping, increments, PLcohortSize = CohortSizeConst(0L), ...)
TDDesign(model, stopping, increments, PLcohortSize = CohortSizeConst(0L), ...)

Arguments

`model`	please refer to `TDDesign` class object
`stopping`	please refer to `TDDesign` class object
`increments`	please refer to `TDDesign` class object
`PLcohortSize`	see `TDDesign`
`...`	additional arguments for `RuleDesign`

Value

the TDDesign class object

Design class using DLE responses only based on the pseudo DLE model without sample

Description

This is a class of design based only on DLE responses using the 'LogisticIndepBeta' class model are used without samples. In addition to the slots in the more simple RuleDesign, objects of this class contain:

Slots

model: the pseudo DLE model to be used, an object class of ModelTox
stopping: stopping rule(s) for the trial, an object class of Stopping
increments: how to control increments between dose levels, an object class of Increments
PLcohortSize: rules for the cohort sizes for placebo, if any planned an object of class CohortSize

Examples

##Specified the design to run simulations
##The design comprises a model, the escalation rule, starting data, 
##a cohort size and a starting dose
##Define your data set first using an empty data set 
## with dose levels from 25 to 300 with increments 25
data <- Data(doseGrid=seq(25,300,25))

##The design only incorporate DLE responses and no DLE samples are involved
##Specified the model of 'ModelTox' class eg 'LogisticIndepBeta' class model
model<-LogisticIndepBeta(binDLE=c(1.05,1.8),DLEweights=c(3,3),DLEdose=c(25,300),data=data)
##Then the escalation rule
tdNextBest <- NextBestTD(targetDuringTrial=0.35,
                         targetEndOfTrial=0.3)

## The cohort size, size of 3 subjects
mySize <-CohortSizeConst(size=3)
##Deifne the increments for the dose-escalation process
##The maximum increase of 200% for doses up to the maximum of the dose specified in the doseGrid
##The maximum increase of 200% for dose above the maximum of the dose specified in the doseGrid
##This is to specified a maximum of 3-fold restriction in dose-esclation
myIncrements<-IncrementsRelative(intervals=c(min(data@doseGrid),max(data@doseGrid)), 
                                 increments=c(2,2))
##Specified the stopping rule e.g stop when the maximum sample size of 36 patients has been reached
myStopping <- StoppingMinPatients(nPatients=36)
##Now specified the design with all the above information and starting with a dose of 25
design <- TDDesign(model=model,
                   nextBest=tdNextBest,
                   stopping=myStopping,
                   increments=myIncrements,
                   cohortSize=mySize,
                   data=data,startingDose=25)
##Specified the design to run simulations
##The design comprises a model, the escalation rule, starting data, 
##a cohort size and a starting dose
##Define your data set first using an empty data set 
## with dose levels from 25 to 300 with increments 25
data <- Data(doseGrid=seq(25,300,25))

##The design only incorporate DLE responses and no DLE samples are involved
##Specified the model of 'ModelTox' class eg 'LogisticIndepBeta' class model
model<-LogisticIndepBeta(binDLE=c(1.05,1.8),DLEweights=c(3,3),DLEdose=c(25,300),data=data)
##Then the escalation rule
tdNextBest <- NextBestTD(targetDuringTrial=0.35,
                         targetEndOfTrial=0.3)

## The cohort size, size of 3 subjects
mySize <-CohortSizeConst(size=3)
##Deifne the increments for the dose-escalation process
##The maximum increase of 200% for doses up to the maximum of the dose specified in the doseGrid
##The maximum increase of 200% for dose above the maximum of the dose specified in the doseGrid
##This is to specified a maximum of 3-fold restriction in dose-esclation
myIncrements<-IncrementsRelative(intervals=c(min(data@doseGrid),max(data@doseGrid)), 
                                 increments=c(2,2))
##Specified the stopping rule e.g stop when the maximum sample size of 36 patients has been reached
myStopping <- StoppingMinPatients(nPatients=36)
##Now specified the design with all the above information and starting with a dose of 25
design <- TDDesign(model=model,
                   nextBest=tdNextBest,
                   stopping=myStopping,
                   increments=myIncrements,
                   cohortSize=mySize,
                   data=data,startingDose=25)

Initialization function for 'TDsamplesDesign' class

Description

Initialization function for 'TDsamplesDesign' class

Usage

TDsamplesDesign(
  model,
  stopping,
  increments,
  PLcohortSize = CohortSizeConst(0L),
  ...
)
TDsamplesDesign(
  model,
  stopping,
  increments,
  PLcohortSize = CohortSizeConst(0L),
  ...
)

Arguments

`model`	see `TDsamplesDesign`
`stopping`	see `TDsamplesDesign`
`increments`	see `TDsamplesDesign`
`PLcohortSize`	see `TDsamplesDesign`
`...`	additional arguments for `RuleDesign`

Value

the TDsamplesDesign class object

This is a class of design based only on DLE responses using the 'LogisticIndepBeta' class model and DLE samples are also used. In addition to the slots in the more simple `RuleDesign`, objects of this class contain:

Description

This is a class of design based only on DLE responses using the 'LogisticIndepBeta' class model and DLE samples are also used. In addition to the slots in the more simple RuleDesign, objects of this class contain:

Slots

model: the pseudo DLE model to be used, an object class of ModelTox
stopping: stopping rule(s) for the trial, an object class of Stopping
increments: how to control increments between dose levels, an object class of Increments
PLcohortSize: rules for the cohort sizes for placebo, if any planned an object of class CohortSize

Examples

##Specified the design to run simulations
##The design comprises a model, the escalation rule, starting data, 
##a cohort size and a starting dose
##Define your data set first using an empty data set 
## with dose levels from 25 to 300 with increments 25
data <- Data(doseGrid=seq(25,300,25))

##The design only incorporate DLE responses and DLE samples are involved
##Specified the model of 'ModelTox' class eg 'LogisticIndepBeta' class model
model<-LogisticIndepBeta(binDLE=c(1.05,1.8),DLEweights=c(3,3),DLEdose=c(25,300),data=data)

samples <- mcmc(data=data, model=model, options=McmcOptions(burnin=100,step=2,samples=200))
##Then the escalation rule
tdNextBest<-NextBestTDsamples(targetDuringTrial=0.35,targetEndOfTrial=0.3,
                              derive=function(TDsamples){quantile(TDsamples,probs=0.3)})
## The cohort size, size of 3 subjects
mySize <-CohortSizeConst(size=3)
##Deifne the increments for the dose-escalation process
##The maximum increase of 200% for doses up to the maximum of the dose specified in the doseGrid
##The maximum increase of 200% for dose above the maximum of the dose specified in the doseGrid
##This is to specified a maximum of 3-fold restriction in dose-esclation
myIncrements<-IncrementsRelative(intervals=c(min(data@doseGrid),max(data@doseGrid)), 
                                 increments=c(2,2))
##Specified the stopping rule e.g stop when the maximum sample size of 36 patients has been reached
myStopping <- StoppingMinPatients(nPatients=36)
##Now specified the design with all the above information and starting with a dose of 25
design <- TDsamplesDesign(model=model,
                          nextBest=tdNextBest,
                          stopping=myStopping,
                          increments=myIncrements,
                          cohortSize=mySize,
                          data=data,startingDose=25)




##Specified the design to run simulations
##The design comprises a model, the escalation rule, starting data, 
##a cohort size and a starting dose
##Define your data set first using an empty data set 
## with dose levels from 25 to 300 with increments 25
data <- Data(doseGrid=seq(25,300,25))

##The design only incorporate DLE responses and DLE samples are involved
##Specified the model of 'ModelTox' class eg 'LogisticIndepBeta' class model
model<-LogisticIndepBeta(binDLE=c(1.05,1.8),DLEweights=c(3,3),DLEdose=c(25,300),data=data)

samples <- mcmc(data=data, model=model, options=McmcOptions(burnin=100,step=2,samples=200))
##Then the escalation rule
tdNextBest<-NextBestTDsamples(targetDuringTrial=0.35,targetEndOfTrial=0.3,
                              derive=function(TDsamples){quantile(TDsamples,probs=0.3)})
## The cohort size, size of 3 subjects
mySize <-CohortSizeConst(size=3)
##Deifne the increments for the dose-escalation process
##The maximum increase of 200% for doses up to the maximum of the dose specified in the doseGrid
##The maximum increase of 200% for dose above the maximum of the dose specified in the doseGrid
##This is to specified a maximum of 3-fold restriction in dose-esclation
myIncrements<-IncrementsRelative(intervals=c(min(data@doseGrid),max(data@doseGrid)), 
                                 increments=c(2,2))
##Specified the stopping rule e.g stop when the maximum sample size of 36 patients has been reached
myStopping <- StoppingMinPatients(nPatients=36)
##Now specified the design with all the above information and starting with a dose of 25
design <- TDsamplesDesign(model=model,
                          nextBest=tdNextBest,
                          stopping=myStopping,
                          increments=myIncrements,
                          cohortSize=mySize,
                          data=data,startingDose=25)

Creates a new 3+3 design object from a dose grid

Description

Creates a new 3+3 design object from a dose grid

Usage

ThreePlusThreeDesign(doseGrid)
ThreePlusThreeDesign(doseGrid)

Arguments

doseGrid

the dose grid to be used

Value

the object of class RuleDesign with the 3+3 design

Author(s)

Daniel Sabanes Bove [email protected]

Examples


# inizialing a 3+3 design 
myDesign <- ThreePlusThreeDesign(doseGrid=c(5, 10, 15, 25, 35, 50, 80))
  
  
# inizialing a 3+3 design 
myDesign <- ThreePlusThreeDesign(doseGrid=c(5, 10, 15, 25, 35, 50, 80))

Update method for the "Data" class

Description

Add new data to the Data object

Usage

## S4 method for signature 'Data'
update(
  object,
  x,
  y,
  ID = (if (length(object@ID)) max(object@ID) else 0L) + seq_along(y),
  newCohort = TRUE,
  ...
)
## S4 method for signature 'Data'
update(
  object,
  x,
  y,
  ID = (if (length(object@ID)) max(object@ID) else 0L) + seq_along(y),
  newCohort = TRUE,
  ...
)

Arguments

`object`	the old `Data` object
`x`	the dose level (one level only!)
`y`	the DLT vector (0/1 vector), for all patients in this cohort
`ID`	the patient IDs
`newCohort`	logical: if TRUE (default) the new data are assigned to a new cohort
`...`	not used

Value

the new Data object

Examples


# Create some data of class 'Data'
myData <- Data(x=c(0.1,0.5,1.5,3,6,10,10,10),
               y=c(0,0,0,0,0,0,1,0),
               doseGrid=c(0.1,0.5,1.5,3,6,
                          seq(from=10,to=80,by=2)))

## update the data with a new cohort
myData <- update(myData,
                 x=20,
                 y=c(0,1,1))


# Create some data of class 'Data'
myData <- Data(x=c(0.1,0.5,1.5,3,6,10,10,10),
               y=c(0,0,0,0,0,0,1,0),
               doseGrid=c(0.1,0.5,1.5,3,6,
                          seq(from=10,to=80,by=2)))

## update the data with a new cohort
myData <- update(myData,
                 x=20,
                 y=c(0,1,1))

Update method for the "DataDual" class

Description

Add new data to the DataDual object

Usage

## S4 method for signature 'DataDual'
update(
  object,
  x,
  y,
  w,
  newCohort = TRUE,
  ID = (if (length(object@ID)) max(object@ID) else 0L) + seq_along(y),
  ...
)
## S4 method for signature 'DataDual'
update(
  object,
  x,
  y,
  w,
  newCohort = TRUE,
  ID = (if (length(object@ID)) max(object@ID) else 0L) + seq_along(y),
  ...
)

Arguments

`object`	the old `DataDual` object
`x`	the dose level (one level only!)
`y`	the DLT vector (0/1 vector), for all patients in this cohort
`w`	the biomarker vector, for all patients in this cohort
`newCohort`	logical: if TRUE (default) the new data are assigned to a new cohort
`ID`	the patient IDs
`...`	not used

Value

the new DataDual object

Examples


# Create some data of class 'DataDual'
myData <- DataDual(x=c(0.1,0.5,1.5,3,6,10,10,10),
                   y=c(0,0,0,0,0,0,1,0),
                   w=rnorm(8),
                   doseGrid=c(0.1,0.5,1.5,3,6,
                              seq(from=10,to=80,by=2)))

## update the data with a new cohort
myData <- update(myData,
                 x=20,
                 y=c(0,1,1),
                 w=c(0.4,1.2,2.2))

# Create some data of class 'DataDual'
myData <- DataDual(x=c(0.1,0.5,1.5,3,6,10,10,10),
                   y=c(0,0,0,0,0,0,1,0),
                   w=rnorm(8),
                   doseGrid=c(0.1,0.5,1.5,3,6,
                              seq(from=10,to=80,by=2)))

## update the data with a new cohort
myData <- update(myData,
                 x=20,
                 y=c(0,1,1),
                 w=c(0.4,1.2,2.2))

Update method for the "DataParts" class

Description

Add new data to the DataParts object

Usage

## S4 method for signature 'DataParts'
update(
  object,
  x,
  y,
  ID = (if (length(object@ID)) max(object@ID) else 0L) + seq_along(y),
  ...
)
## S4 method for signature 'DataParts'
update(
  object,
  x,
  y,
  ID = (if (length(object@ID)) max(object@ID) else 0L) + seq_along(y),
  ...
)

Arguments

`object`	the old `DataParts` object
`x`	the dose level (one level only!)
`y`	the DLT vector (0/1 vector), for all patients in this cohort
`ID`	the patient IDs
`...`	not used

Value

the new DataParts object

Examples


# create an object of class 'DataParts'
myData <- DataParts(x=c(0.1,0.5,1.5),
                    y=c(0,0,0),
                    doseGrid=c(0.1,0.5,1.5,3,6,
                               seq(from=10,to=80,by=2)),
                    part=c(1L,1L,1L),
                    nextPart=1L,
                    part1Ladder=c(0.1,0.5,1.5,3,6,10))

# update the data with a new cohort
# to be noted that since we reached the last level from part1Ladder then
# nextPart is switched from '1' to '2'
myData <- update(myData,
                 x=10,
                 y=c(0))


# create an object of class 'DataParts'
myData <- DataParts(x=c(0.1,0.5,1.5),
                    y=c(0,0,0),
                    doseGrid=c(0.1,0.5,1.5,3,6,
                               seq(from=10,to=80,by=2)),
                    part=c(1L,1L,1L),
                    nextPart=1L,
                    part1Ladder=c(0.1,0.5,1.5,3,6,10))

# update the data with a new cohort
# to be noted that since we reached the last level from part1Ladder then
# nextPart is switched from '1' to '2'
myData <- update(myData,
                 x=10,
                 y=c(0))

Update method for the 'EffFlexi' Model class. This is a method to update estimates both for the flexible form model and the random walk model (see details in `EffFlexi` class object) when new data or new observations of responses are available and added in.

Description

Update method for the 'EffFlexi' Model class. This is a method to update estimates both for the flexible form model and the random walk model (see details in EffFlexi class object) when new data or new observations of responses are available and added in.

Usage

## S4 method for signature 'EffFlexi'
update(object, data, ...)
## S4 method for signature 'EffFlexi'
update(object, data, ...)

Arguments

`object`	is the model which follow `EffFlexi` class object
`data`	all currently available data and responses of `DataDual` class object
`...`	unused

Value

the new EffFlexi class object

Examples

##Update the 'EffFlexi' model with new data
## first define the data and the model
emptydata<-DataDual(doseGrid=seq(25,300,25))
data<-emptydata

Effmodel<- EffFlexi(Eff=c(1.223, 2.513),Effdose=c(25,300),
                    sigma2=c(a=0.1,b=0.1),sigma2betaW=c(a=20,b=50),smooth="RW2",data=data)


##Then we have some new observations data
data<-DataDual(x=c(25,50,50,75,100,100,225,300),y=c(0,0,0,0,1,1,1,1),
               w=c(0.31,0.42,0.59,0.45,0.6,0.7,0.6,0.52),
               doseGrid=seq(25,300,25))

##update the model to get new estimates
newEffModel <- update(object=Effmodel,data=data)
##Update the 'EffFlexi' model with new data
## first define the data and the model
emptydata<-DataDual(doseGrid=seq(25,300,25))
data<-emptydata

Effmodel<- EffFlexi(Eff=c(1.223, 2.513),Effdose=c(25,300),
                    sigma2=c(a=0.1,b=0.1),sigma2betaW=c(a=20,b=50),smooth="RW2",data=data)


##Then we have some new observations data
data<-DataDual(x=c(25,50,50,75,100,100,225,300),y=c(0,0,0,0,1,1,1,1),
               w=c(0.31,0.42,0.59,0.45,0.6,0.7,0.6,0.52),
               doseGrid=seq(25,300,25))

##update the model to get new estimates
newEffModel <- update(object=Effmodel,data=data)

Update method for the 'Effloglog' Model class. This is a method to update the modal estimates of the model parameters $\theta_1$ (theta1), $\theta_2$ (theta2) and $\nu$ (nu, the precision of the efficacy responses) when new data or new observations of responses are available and added in.

Description

Update method for the 'Effloglog' Model class. This is a method to update the modal estimates of the model parameters $\theta_1$ (theta1), $\theta_2$ (theta2) and $\nu$ (nu, the precision of the efficacy responses) when new data or new observations of responses are available and added in.

Usage

## S4 method for signature 'Effloglog'
update(object, data, ...)
## S4 method for signature 'Effloglog'
update(object, data, ...)

Arguments

`object`	the `Effloglog` class object
`data`	all currently available data or responses of `DataDual` class object
`...`	unused

Value

the new Effloglog class object

Examples

##Update the 'Effloglog' model with new data
## first define the data and the model
emptydata<-DataDual(doseGrid=seq(25,300,25),placebo=FALSE)
data<-emptydata

Effmodel<-Effloglog(Eff=c(1.223,2.513),Effdose=c(25,300),nu=c(a=1,b=0.025),data=data,c=0)

##Then we have some new observations data
data<-DataDual(x=c(25,50,50,75,100,100,225,300),y=c(0,0,0,0,1,1,1,1),
               w=c(0.31,0.42,0.59,0.45,0.6,0.7,0.6,0.52),
               doseGrid=seq(25,300,25))

##update the model to get new estimates
newEffModel <- update(object=Effmodel,data=data)
##Update the 'Effloglog' model with new data
## first define the data and the model
emptydata<-DataDual(doseGrid=seq(25,300,25),placebo=FALSE)
data<-emptydata

Effmodel<-Effloglog(Eff=c(1.223,2.513),Effdose=c(25,300),nu=c(a=1,b=0.025),data=data,c=0)

##Then we have some new observations data
data<-DataDual(x=c(25,50,50,75,100,100,225,300),y=c(0,0,0,0,1,1,1,1),
               w=c(0.31,0.42,0.59,0.45,0.6,0.7,0.6,0.52),
               doseGrid=seq(25,300,25))

##update the model to get new estimates
newEffModel <- update(object=Effmodel,data=data)

Update method for the 'LogisticIndepBeta'Model class. This is a method to update the modal estimates of the model parameters $\phi_1$ (phi1) and $\phi_2$ (phi2) when new data or new observations of responses are available and added in.

Description

Update method for the 'LogisticIndepBeta'Model class. This is a method to update the modal estimates of the model parameters $\phi_1$ (phi1) and $\phi_2$ (phi2) when new data or new observations of responses are available and added in.

Usage

## S4 method for signature 'LogisticIndepBeta'
update(object, data, ...)
## S4 method for signature 'LogisticIndepBeta'
update(object, data, ...)

Arguments

`object`	the model of `LogisticIndepBeta` class object
`data`	all currently availabvle of `Data` class object
`...`	unused

Value

the new LogisticIndepBeta class object

Examples

##Update the 'LogisticIndepBeta' model with new data
## first define the data and the model
emptydata<-Data(doseGrid=seq(25,300,25))
data<-emptydata

model<-LogisticIndepBeta(binDLE=c(1.05,1.8),DLEweights=c(3,3),DLEdose=c(25,300),data=data)
##Then we have some new observations data
data<-Data(x=c(25,50,50,75,100,100,225,300),
           y=c(0,0,0,0,1,1,1,1),
           doseGrid=seq(from=25,to=300,by=25))
##update the model to get new estimates
newModel <- update(object=model,data=data)
##Update the 'LogisticIndepBeta' model with new data
## first define the data and the model
emptydata<-Data(doseGrid=seq(25,300,25))
data<-emptydata

model<-LogisticIndepBeta(binDLE=c(1.05,1.8),DLEweights=c(3,3),DLEdose=c(25,300),data=data)
##Then we have some new observations data
data<-Data(x=c(25,50,50,75,100,100,225,300),
           y=c(0,0,0,0,1,1,1,1),
           doseGrid=seq(from=25,to=300,by=25))
##update the model to get new estimates
newModel <- update(object=model,data=data)

A Reference Class to help programming validation for new S4 classes

Description

Starting from an empty msg vector, with each check that is returning FALSE the vector gets a new element - the string explaining the failure of the validation

Fields

msg: the message character vector

Creating a WinBUGS model file

Description

Convert R function to a WinBUGS model file. BUGS models follow closely S syntax. It is therefore possible to write most BUGS models as R functions. As a difference, BUGS syntax allows truncation specification like this: dnorm(...) I(...) but this is illegal in R. To overcome this incompatibility, use dummy operator %_% before I(...): dnorm(...) %_% I(...). The dummy operator %_% will be removed before the BUGS code is saved. In S-PLUS, a warning is generated when the model function is defined if the last statement in the model is an assignment. To avoid this warning, add the line invisible() to the end of the model definition. This line will be removed before the BUGS code is saved.

Usage

writeModel(model, con = "model.bug", digits = 5)
writeModel(model, con = "model.bug", digits = 5)

Arguments

`model`	R function containing the BUGS model in the BUGS model language, for minor differences see Section Details.
`con`	passed to `writeLines` which actually writes the model file
`digits`	number of significant digits used for WinBUGS input, see `formatC`

Value

Nothing, but as a side effect, the model file is written

Author(s)

original idea by Jouni Kerman, modified by Uwe Ligges, Daniel Sabanes Bove removed S-PLUS part

Package 'crmPack'

Help Index

Object-oriented implementation of CRM designs

Description

Author(s)

References

The method combining two atomic stopping rules

Description

Usage

Arguments

Value

Examples

The method combining an atomic and a stopping list

Description

Usage

Arguments

Value

Examples

The method combining a stopping list and an atomic

Description

Usage

Arguments

Value

Examples

Class for All models This is a class where all models inherit.

Description

Slots

See Also

Approximate posterior with (log) normal distribution

Description

Usage

Arguments

Value

Functions

Examples

as.list method for the "GeneralData" class

Description

Usage

Arguments

Value

Examples

Compute the biomarker level for a given dose, given model and samples

Description

Usage

Arguments

Functions

Examples

The virtual class for cohort sizes

Description

See Also

Initialization function for "CohortSizeConst"

Description

Usage

Arguments

Value

Constant cohort size

Description

Slots

Examples

Initialization function for "CohortSizeDLT"

Description

Usage

Arguments

Value

Cohort size based on number of DLTs

Description

Slots

Examples

Initialization function for "CohortSizeMax"

Description

Usage

Arguments

Value

Size based on maximum of multiple cohort size rules

Description

Details

Slots

Examples

Initialization function for "CohortSizeMin"

Description