Package 'boinet' reference manual

Title:	Conduct Simulation Study of Bayesian Optimal Interval Design with BOIN-ET Family
Description:	Bayesian optimal interval based on both efficacy and toxicity outcomes (BOIN-ET) design is a model-assisted oncology phase I/II trial design, aiming to establish an optimal biological dose accounting for efficacy and toxicity in the framework of dose-finding. Some extensions of BOIN-ET design are also available to allow for time-to-event efficacy and toxicity outcomes based on cumulative and pending data (time-to-event BOIN-ET: TITE-BOIN-ET), ordinal graded efficacy and toxicity outcomes (generalized BOIN-ET: gBOIN-ET), and their combination (TITE-gBOIN-ET). 'boinet' is a package to implement the BOIN-ET design family and supports the conduct of simulation studies to assess operating characteristics of BOIN-ET, TITE-BOIN-ET, gBOIN-ET, and TITE-gBOIN-ET, where users can choose design parameters in flexible and straightforward ways depending on their own application.
Authors:	Yusuke Yamaguchi [aut, cre], Kentaro Takeda [aut]
Maintainer:	Yusuke Yamaguchi <[email protected]>
License:	MIT + file LICENSE
Version:	1.1.0
Built:	2024-11-23 06:22:17 UTC
Source:	CRAN

Conducting simulation study of BOIN-ET design

Description

Bayesian optimal interval design for dose finding based on both efficacy and toxicity outcomes (BOIN-ET design) is implemented under a scenario specified. Operating characteristics of the design are summarized by the percentage of times that each dose level was selected as optimal biological dose and the average number of patients who were treated at each dose level.

Usage

boinet(
  n.dose, start.dose, size.cohort, n.cohort,
  toxprob, effprob,
  phi=0.3, phi1=phi*0.1, phi2=phi*1.4, delta=0.6, delta1=delta*0.6,
  alpha.T1=0.5, alpha.E1=0.5, tau.T, tau.E,
  te.corr=0.2, gen.event.time="weibull",
  accrual, gen.enroll.time="uniform",
  stopping.npts=size.cohort*n.cohort,
  stopping.prob.T=0.95, stopping.prob.E=0.99,
  estpt.method, obd.method,
  w1= 0.33, w2=1.09,
  plow.ast=phi1, pupp.ast=phi2, qlow.ast=delta1/2, qupp.ast=delta,
  psi00=40, psi11=60,
  n.sim=1000, seed.sim=100)
boinet(
  n.dose, start.dose, size.cohort, n.cohort,
  toxprob, effprob,
  phi=0.3, phi1=phi*0.1, phi2=phi*1.4, delta=0.6, delta1=delta*0.6,
  alpha.T1=0.5, alpha.E1=0.5, tau.T, tau.E,
  te.corr=0.2, gen.event.time="weibull",
  accrual, gen.enroll.time="uniform",
  stopping.npts=size.cohort*n.cohort,
  stopping.prob.T=0.95, stopping.prob.E=0.99,
  estpt.method, obd.method,
  w1= 0.33, w2=1.09,
  plow.ast=phi1, pupp.ast=phi2, qlow.ast=delta1/2, qupp.ast=delta,
  psi00=40, psi11=60,
  n.sim=1000, seed.sim=100)

Arguments

`n.dose`	Number of dose.
`start.dose`	Starting dose. The lowest dose is generally recommended.
`size.cohort`	Cohort size.
`n.cohort`	Number of cohort.
`toxprob`	Vector of true toxicity probability.
`effprob`	Vector of true efficacy probability.
`phi`	Target toxicity probability. The default value is `phi=0.3`.
`phi1`	Highest toxicity probability that is deemed sub-therapeutic such that dose-escalation should be pursued. The default value is `phi1=phi*0.1`.
`phi2`	Lowest toxicity probability that is deemed overly toxic such that dose de-escalation is needed. The default value is `phi2=phi*1.4`.
`delta`	Target efficacy probability. The default value is `delta=0.6`.
`delta1`	Minimum probability deemed efficacious such that the dose levels with less than delta1 are considered sub-therapeutic. The default value is `delta1=delta*0.6`.
`alpha.T1`	Probability that toxicity event occurs in the late half of toxicity assessment window. The default value is `alpha.T1=0.5`.
`alpha.E1`	Probability that efficacy event occurs in the late half of assessment window. The default value is `alpha.E1=0.5`.
`tau.T`	Toxicity assessment windows (days).
`tau.E`	Efficacy assessment windows (days).
`te.corr`	Correlation between toxicity and efficacy probability, specified as Gaussian copula parameter. The default value is `te.corr=0.2`.
`gen.event.time`	Method to generate the time to first toxicity and efficacy outcome. Weibull distribution is used when `gen.event.time="weibull"`. Uniform distribution is used when `gen.event.time="uniform"`. The default value is `gen.event.time="weibull"`.
`accrual`	Accrual rate (days) (average number of days necessary to enroll one patient).
`gen.enroll.time`	Method to generate enrollment time. Uniform distribution is used when `gen.enroll.time="uniform"`. Exponential distribution is used when `gen.enroll.time="exponential"`. The default value is `gen.enroll.time="uniform"`.
`stopping.npts`	Early study termination criteria for the number of patients. If the number of patients at the current dose reaches this criteria, the study is terminated. The default value is `stopping.npts=size.cohort*n.cohort`.
`stopping.prob.T`	Early study termination criteria for toxicity, taking a value between 0 and 1. If the posterior probability that toxicity outcome is less than the target toxicity probability (`phi`) is larger than this criteria, the dose levels are eliminated from the study. The default value is `stopping.prob.T=0.95`.
`stopping.prob.E`	Early study termination criteria for efficacy, taking a value between 0 and 1. If the posterior probability that efficacy outcome is less than the minimum efficacy probability (`delta1`) is larger than this criteria, the dose levels are eliminated from the study. The default value is `stopping.prob.E=0.99`.
`estpt.method`	Method to estimate the efficacy probability. Fractional polynomial logistic regression is used when `estpt.method="fp.logistic"`. Model averaging of multiple unimodal isotopic regression is used when `estpt.method="multi.iso"`. Observed efficacy probability is used when `estpt.method="obs.prob"`.
`obd.method`	Method to select the optimal biological dose. Utility defined by weighted function is used when `obd.method="utility.weighted"`. Utility defined by truncated linear function is used when `obd.method="utility.truncated.linear"`. Utility defined by scoring is used when `obd.method="utility.scoring"`. Highest estimated efficacy probability is used when `obd.method="max.effprob"`.
`w1`	Weight for toxicity-efficacy trade-off in utility defined by weighted function. This must be specified when using `obd.method="utility.weighted"`. The default value is `w1=0.33`.
`w2`	Weight for penalty imposed on toxic doses in utility defined by weighted function. This must be specified when using `obd.method="utility.weighted"`. The default value is `w2=1.09`.
`plow.ast`	Lower threshold of toxicity linear truncated function. This must be specified when using `obd.method="utility.truncated.linear"`. The default value is `plow.ast=phi1`.
`pupp.ast`	Upper threshold of toxicity linear truncated function. This must be specified when using `obd.method="utility.truncated.linear"`. The default value is `pupp.ast=phi2`.
`qlow.ast`	Lower threshold of efficacy linear truncated function. This must be specified when using `obd.method="utility.truncated.linear"`. The default value is `qlow.ast=delta1/2`.
`qupp.ast`	Upper threshold of efficacy linear truncated function. This must be specified when using `obd.method="utility.truncated.linear"`. The default value is `qupp.ast=delta`.
`psi00`	Score for toxicity=no and efficacy=no in utility defined by scoring. This must be specified when using `obd.method="utility.scoring"`. The default value is `psi00=40`.
`psi11`	Score for toxicity=yes and efficacy=yes in utility defined by scoring. This must be specified when using `obd.method="utility.scoring"`. The default value is `psi11=60`.
`n.sim`	Number of simulated trial. The default value is `n.sim=1000`.
`seed.sim`	Seed for random number generator. The default value is `seed.sim=100`.

Details

The boinet is a function which generates the operating characteristics of the Bayesian Optimal Interval design based on toxicity and efficacy (BOIN-ET design) by a simulation study. Users can specify a variety of study settings to simulate studies, and choose methods to estimate the efficacy probability and to select the optimal biological dose. The operating characteristics of the design are summarized by the percentage of times that each dose level was selected as optimal biological dose and the average number of patients who were treated at each dose level. The percentage of times that the study was terminated and the expected study duration are also provided.

Value

The boinet returns a list containing the following components:

`toxprob`	True toxicity probability.
`effprob`	True efficacy probability.
`phi`	Target toxicity probability.
`delta`	Target efficacy probability.
`lambda1`	Lower toxicity boundary in dose escalation/de-escalation.
`lambda2`	Upper toxicity boundary in dose escalation/de-escalation.
`eta1`	Lower efficacy boundary in dose escalation/de-escalation.
`tau.T`	Toxicity assessment windows (days).
`tau.E`	Efficacy assessment windows (days).
`accrual`	Accrual rate (days) (average number of days necessary to enroll one patient).
`estpt.method`	Method to estimate the efficacy probability.
`obd.method`	Method to select the optimal biological dose.
`n.patient`	Average number of patients who were treated at each dose level
`prop.select`	Percentage of times that each dose level was selected as optimal biological dose.
`prop.stop`	Percentage of times that the study was terminated and optimal biological dose was not selected.
`duration`	Expected study duration (days)

References

Takeda K, Taguri M, Morita S. BOIN-ET: Bayesian optimal interval design for dose finding based on both efficacy and toxicity outcomes. Pharmaceutical Statistics 2018; 17(4):383-395.

Yamaguchi Y, Takeda K, Yoshida S, Maruo K. Optimal biological dose selection in dose-finding trials with model-assisted designs based on efficacy and toxicity: a simulation study. Journal of Biopharmaceutical Statistics 2023; doi: 10.1080/10543406.2023.2202259.

Examples

n.dose      <- 6
start.dose  <- 1
size.cohort <- 3
n.cohort    <- 12

toxprob <- c(0.01,0.03,0.06,0.12,0.18,0.30)
effprob <- c(0.06,0.08,0.15,0.25,0.40,0.80)

phi   <- 0.33
delta <- 0.70

tau.T   <- 30
tau.E   <- 45
accrual <- 10

estpt.method <- "obs.prob"
obd.method   <- "max.effprob"

n.sim <- 10

boinet(
  n.dose=n.dose, start.dose=start.dose,
  size.cohort=size.cohort, n.cohort=n.cohort,
  toxprob=toxprob, effprob=effprob,
  phi=phi, delta=delta,
  tau.T=tau.T, tau.E=tau.E, accrual=accrual,
  estpt.method=estpt.method, obd.method=obd.method,
  n.sim=n.sim)
n.dose      <- 6
start.dose  <- 1
size.cohort <- 3
n.cohort    <- 12

toxprob <- c(0.01,0.03,0.06,0.12,0.18,0.30)
effprob <- c(0.06,0.08,0.15,0.25,0.40,0.80)

phi   <- 0.33
delta <- 0.70

tau.T   <- 30
tau.E   <- 45
accrual <- 10

estpt.method <- "obs.prob"
obd.method   <- "max.effprob"

n.sim <- 10

boinet(
  n.dose=n.dose, start.dose=start.dose,
  size.cohort=size.cohort, n.cohort=n.cohort,
  toxprob=toxprob, effprob=effprob,
  phi=phi, delta=delta,
  tau.T=tau.T, tau.E=tau.E, accrual=accrual,
  estpt.method=estpt.method, obd.method=obd.method,
  n.sim=n.sim)

Fractional polynomial logistic regression

Description

Fractional polynomial (FP) logistic regression with two degrees of freedom is performed to estimate the efficacy probabilities. The Best fitting FP model is chosen by not taking into account the closed testing procedure.

Usage

fp.logit(obs, n, dose)
fp.logit(obs, n, dose)

Arguments

`obs`	Number of patients with events.
`n`	Number of patients.
`dose`	Dose levels to be investigated.

Value

The fp.logit returns a vector of estimated probabilities for each dose level.

Conducting simulation study of gBOIN-ET design

Description

Generalized Bayesian optimal interval design for optimal dose-finding accounting for ordinal graded efficacy and toxicity (gBOIN-ET design) is implemented under a scenario specified. Operating characteristics of the design are summarized by the percentage of times that each dose level was selected as optimal biological dose and the average number of patients who were treated at each dose level.

Usage

gboinet(
  n.dose, start.dose, size.cohort, n.cohort,
  toxprob, effprob, sev.weight, res.weight,
  phi, phi1=phi*0.1, phi2=phi*1.4, delta, delta1=delta*0.6,
  alpha.T1=0.5, alpha.E1=0.5, tau.T, tau.E,
  te.corr=0.2, gen.event.time="weibull",
  accrual, gen.enroll.time="uniform",
  stopping.npts=size.cohort*n.cohort,
  stopping.prob.T=0.95, stopping.prob.E=0.99,
  estpt.method, obd.method,
  w1=0.33, w2=1.09,
  plow.ast=phi1, pupp.ast=phi2, qlow.ast=delta1/2, qupp.ast=delta,
  psi00=40, psi11=60,
  n.sim=1000, seed.sim=100)
gboinet(
  n.dose, start.dose, size.cohort, n.cohort,
  toxprob, effprob, sev.weight, res.weight,
  phi, phi1=phi*0.1, phi2=phi*1.4, delta, delta1=delta*0.6,
  alpha.T1=0.5, alpha.E1=0.5, tau.T, tau.E,
  te.corr=0.2, gen.event.time="weibull",
  accrual, gen.enroll.time="uniform",
  stopping.npts=size.cohort*n.cohort,
  stopping.prob.T=0.95, stopping.prob.E=0.99,
  estpt.method, obd.method,
  w1=0.33, w2=1.09,
  plow.ast=phi1, pupp.ast=phi2, qlow.ast=delta1/2, qupp.ast=delta,
  psi00=40, psi11=60,
  n.sim=1000, seed.sim=100)

Arguments

`n.dose`	Number of dose.
`start.dose`	Starting dose. The lowest dose is generally recommended.
`size.cohort`	Cohort size.
`n.cohort`	Number of cohort.
`toxprob`	Vector of true toxicity probability.
`effprob`	Vector of true efficacy probability.
`sev.weight`	Vector of weight for toxicity category.
`res.weight`	Vector of weight for efficacy category.
`phi`	Target toxicity probability.
`phi1`	Highest toxicity probability that is deemed sub-therapeutic such that dose-escalation should be pursued. The default value is `phi1=phi*0.1`.
`phi2`	Lowest toxicity probability that is deemed overly toxic such that dose de-escalation is needed. The default value is `phi2=phi*1.4`.
`delta`	Target efficacy probability.
`delta1`	Minimum probability deemed efficacious such that the dose levels with less than delta1 are considered sub-therapeutic. The default value is `delta1=delta*0.6`.
`alpha.T1`	Probability that toxicity event occurs in the late half of toxicity assessment window. The default value is `alpha.T1=0.5`.
`alpha.E1`	Probability that efficacy event occurs in the late half of assessment window. The default value is `alpha.E1=0.5`.
`tau.T`	Toxicity assessment windows (days).
`tau.E`	Efficacy assessment windows (days).
`te.corr`	Correlation between toxicity and efficacy probability, specified as Gaussian copula parameter. The default value is `te.corr=0.2`.
`gen.event.time`	Method to generate the time to first toxicity and efficacy outcome. Weibull distribution is used when `gen.event.time="weibull"`. Uniform distribution is used when `gen.event.time="uniform"`. The default value is `gen.event.time="weibull"`.
`accrual`	Accrual rate (days) (average number of days necessary to enroll one patient).
`gen.enroll.time`	Method to generate enrollment time. Uniform distribution is used when `gen.enroll.time="uniform"`. Exponential distribution is used when `gen.enroll.time="exponential"`. The default value is `gen.enroll.time="uniform"`.
`stopping.npts`	Early study termination criteria for the number of patients. If the number of patients at the current dose reaches this criteria, the study is terminated. The default value is `stopping.npts=size.cohort*n.cohort`.
`stopping.prob.T`	Early study termination criteria for toxicity, taking a value between 0 and 1. If the posterior probability that toxicity outcome is less than the target toxicity probability (`phi`) is larger than this criteria, the dose levels are eliminated from the study. The default value is `stopping.prob.T=0.95`.
`stopping.prob.E`	Early study termination criteria for efficacy, taking a value between 0 and 1. If the posterior probability that efficacy outcome is less than the minimum efficacy probability (`delta1`) is larger than this criteria, the dose levels are eliminated from the study. The default value is `stopping.prob.E=0.99`.
`estpt.method`	Method to estimate the efficacy probability. Fractional polynomial logistic regression is used when `estpt.method="fp.logistic"`. Model averaging of multiple unimodal isotopic regression is used when `estpt.method="multi.iso"`. Observed efficacy probability is used when `estpt.method="obs.prob"`.
`obd.method`	Method to select the optimal biological dose. Utility defined by weighted function is used when `obd.method="utility.weighted"`. Utility defined by truncated linear function is used when `obd.method="utility.truncated.linear"`. Utility defined by scoring is used when `obd.method="utility.scoring"`. Highest estimated efficacy probability is used when `obd.method="max.effprob"`.
`w1`	Weight for toxicity-efficacy trade-off in utility defined by weighted function. This must be specified when using `obd.method="utility.weighted"`. The default value is `w1=0.33`.
`w2`	Weight for penalty imposed on toxic doses in utility defined by weighted function. This must be specified when using `obd.method="utility.weighted"`. The default value is `w2=1.09`.
`plow.ast`	Lower threshold of toxicity linear truncated function. This must be specified when using `obd.method="utility.truncated.linear"`. The default value is `plow.ast=phi1`.
`pupp.ast`	Upper threshold of toxicity linear truncated function. This must be specified when using `obd.method="utility.truncated.linear"`. The default value is `pupp.ast=phi2`.
`qlow.ast`	Lower threshold of efficacy linear truncated function. This must be specified when using `obd.method="utility.truncated.linear"`. The default value is `qlow.ast=delta1/2`.
`qupp.ast`	Upper threshold of efficacy linear truncated function. This must be specified when using `obd.method="utility.truncated.linear"`. The default value is `qupp.ast=delta`.
`psi00`	Score for toxicity=no and efficacy=no in utility defined by scoring. This must be specified when using `obd.method="utility.scoring"`. The default value is `psi00=40`.
`psi11`	Score for toxicity=yes and efficacy=yes in utility defined by scoring. This must be specified when using `obd.method="utility.scoring"`. The default value is `psi11=60`.
`n.sim`	Number of simulated trial. The default value is `n.sim=1000`.
`seed.sim`	Seed for random number generator. The default value is `seed.sim=100`.

Details

The gboinet is a function which generates the operating characteristics of the generalized Bayesian optimal interval design for optimal dose-finding accounting for ordinal graded efficacy and toxicity (gBOIN-ET design) by a simulation study. Users can specify a variety of study settings to simulate studies, and choose methods to estimate the efficacy probability and to select the optimal biological dose. The operating characteristics of the design are summarized by the percentage of times that each dose level was selected as optimal biological dose and the average number of patients who were treated at each dose level. The percentage of times that the study was terminated and the expected study duration are also provided.

Value

The gboinet returns a list containing the following components:

`toxprob`	True toxicity probability.
`effprob`	True efficacy probability.
`nETS`	Normalized equivalent toxicity score.
`nEES`	Normalized equivalent efficacy score.
`phi`	Target toxicity probability.
`delta`	Target efficacy probability.
`lambda1`	Lower toxicity boundary in dose escalation/de-escalation.
`lambda2`	Upper toxicity boundary in dose escalation/de-escalation.
`eta1`	Lower efficacy boundary in dose escalation/de-escalation.
`tau.T`	Toxicity assessment windows (days).
`tau.E`	Efficacy assessment windows (days).
`accrual`	Accrual rate (days) (average number of days necessary to enroll one patient).
`estpt.method`	Method to estimate the efficacy probability.
`obd.method`	Method to select the optimal biological dose.
`n.patient`	Average number of patients who were treated at each dose level
`prop.select`	Percentage of times that each dose level was selected as optimal biological dose.
`prop.stop`	Percentage of times that the study was terminated and optimal biological dose was not selected.
`duration`	Expected study duration (days)

References

Takeda K, Morita S, Taguri M. gBOIN-ET: The generalized Bayesian optimal interval design for optimal dose-finding accounting for ordinal graded efficacy and toxicity in early clinical trials. Biometrical Journal 2022: 64(7):1178-1191.

Examples

n.dose      <- 6
start.dose  <- 1
size.cohort <- 3
n.cohort    <- 12

toxprob <- rbind(c(0.94,0.87,0.79,0.68,0.62,0.50),
                 c(0.05,0.10,0.15,0.20,0.20,0.20),
                 c(0.01,0.03,0.05,0.10,0.15,0.25),
                 c(0.00,0.00,0.01,0.02,0.03,0.05))
effprob <- rbind(c(0.64,0.52,0.45,0.35,0.20,0.05),
                 c(0.30,0.40,0.40,0.40,0.40,0.15),
                 c(0.05,0.05,0.10,0.15,0.20,0.35),
                 c(0.01,0.03,0.05,0.10,0.20,0.45))

sev.weight <- c(0.00,0.50,1.00,1.50)
res.weight <- c(0.00,0.25,1.00,3.00)

phi   <- 0.33
delta <- 0.70

tau.T   <- 30
tau.E   <- 45
accrual <- 10

estpt.method <- "obs.prob"
obd.method   <- "max.effprob"

n.sim <- 10

gboinet(
  n.dose=n.dose, start.dose=start.dose,
  size.cohort=size.cohort, n.cohort=n.cohort,
  toxprob=toxprob, effprob=effprob,
  sev.weight=sev.weight, res.weight=res.weight,
  phi=phi, delta=delta,
  tau.T=tau.T, tau.E=tau.E, accrual=accrual,
  estpt.method=estpt.method, obd.method=obd.method,
  n.sim=n.sim)
n.dose      <- 6
start.dose  <- 1
size.cohort <- 3
n.cohort    <- 12

toxprob <- rbind(c(0.94,0.87,0.79,0.68,0.62,0.50),
                 c(0.05,0.10,0.15,0.20,0.20,0.20),
                 c(0.01,0.03,0.05,0.10,0.15,0.25),
                 c(0.00,0.00,0.01,0.02,0.03,0.05))
effprob <- rbind(c(0.64,0.52,0.45,0.35,0.20,0.05),
                 c(0.30,0.40,0.40,0.40,0.40,0.15),
                 c(0.05,0.05,0.10,0.15,0.20,0.35),
                 c(0.01,0.03,0.05,0.10,0.20,0.45))

sev.weight <- c(0.00,0.50,1.00,1.50)
res.weight <- c(0.00,0.25,1.00,3.00)

phi   <- 0.33
delta <- 0.70

tau.T   <- 30
tau.E   <- 45
accrual <- 10

estpt.method <- "obs.prob"
obd.method   <- "max.effprob"

n.sim <- 10

gboinet(
  n.dose=n.dose, start.dose=start.dose,
  size.cohort=size.cohort, n.cohort=n.cohort,
  toxprob=toxprob, effprob=effprob,
  sev.weight=sev.weight, res.weight=res.weight,
  phi=phi, delta=delta,
  tau.T=tau.T, tau.E=tau.E, accrual=accrual,
  estpt.method=estpt.method, obd.method=obd.method,
  n.sim=n.sim)

Grid search to find optimal threshold values of toxicity and efficacy interval

Description

Given non-informative prior probabilities of the six hypotheses, a grid search approach is used to find the optimal threshold values.

Usage

gridoptim(pi=rep(1/6,6), phi, phi1, phi2, delta, delta1, n=100)
gridoptim(pi=rep(1/6,6), phi, phi1, phi2, delta, delta1, n=100)

Arguments

`pi`	Prior probability of 6 hypotheses. The default value is `pi=rep(1/6,6)`.
`phi`	Target toxicity probability.
`phi1`	Lower bound of toxicity probability.
`phi2`	Upper bound of toxicity probability.
`delta`	Target efficacy probability.
`delta1`	Lower bound of efficacy probability.
`n`	Number of patients. The default value is `n=100`.

Value

The gridoptim returns optimal threshold values of upper and lower toxicity/efficacy boundaries used in dose-escalation procedure.

Examples

gridoptim(phi=0.33,phi1=0.033,phi2=0.462,delta=0.70,delta1=0.42);
gridoptim(phi=0.33,phi1=0.033,phi2=0.462,delta=0.70,delta1=0.42);

Model averaging of multiple unimodal isotopic regression

Description

Given the location of the mode to be at each dose level, the unimodal isotonically transformed values are calculated. A frequentist model averaging approach is used to obtain the estimated efficacy probability.

Usage

multi.iso(obs, n)
multi.iso(obs, n)

Arguments

`obs`	Number of patients with events.
`n`	Number of patients.

Value

The multi.iso returns a vector of estimated probabilities for each dose level.

Optimal biological dose selection

Description

Optimal biological dose (OBD) is selected by a method specified.

Usage

obd.select(
  probt, probe, method,
  phi, phi1, phi2, delta, delta1,
  tterm, eterm, stopT, stopE,
  w1, w2,
  plow.ast, pupp.ast, qlow.ast, qupp.ast,
  psi00, psi11)
obd.select(
  probt, probe, method,
  phi, phi1, phi2, delta, delta1,
  tterm, eterm, stopT, stopE,
  w1, w2,
  plow.ast, pupp.ast, qlow.ast, qupp.ast,
  psi00, psi11)

Arguments

`probt`	Estimated toxicity probability.
`probe`	Estimated efficacy probability.
`method`	Method used for OBD selection.
`phi`	Target toxicity probability.
`phi1`	Lower bound of toxicity probability.
`phi2`	Upper bound of toxicity probability.
`delta`	Target efficacy probability.
`delta1`	Lower bound of efficacy probability.
`tterm`	Probability of meeting toxicity stopping criteria.
`eterm`	Probability of meeting efficacy stopping criteria.
`stopT`	Toxicity stopping criteria.
`stopE`	Efficacy stopping criteria.
`w1`	Weight for toxicity-efficacy trade-off.
`w2`	Weight for penalty imposed on toxic doses.
`plow.ast`	Lower threshold of toxicity linear truncated function.
`pupp.ast`	Upper threshold of toxicity linear truncated function.
`qlow.ast`	Lower threshold of efficacy linear truncated function.
`qupp.ast`	Upper threshold of efficacy linear truncated function.
`psi00`	Score for toxicity=no and efficacy=no.
`psi11`	Score for toxicity=yes and efficacy=yes.

Value

The obd.select returns an optimal biological dose.

Print boinet

Description

Display key summary results from boinet.

Usage

## S3 method for class 'boinet'
print(x, ...)
## S3 method for class 'boinet'
print(x, ...)

Arguments

`x`	Object from `boinet`.
`...`	More options to pass to print.

Value

No return values. Key summary results from boinet are displayed with trial design settings.

Print gboinet

Description

Display key summary results from gboinet.

Usage

## S3 method for class 'gboinet'
print(x, ...)
## S3 method for class 'gboinet'
print(x, ...)

Arguments

`x`	Object from `gboinet`.
`...`	More options to pass to print.

Value

No return values. Key summary results from gboinet are displayed with trial design settings.

Print tite.boinet

Description

Display key summary results from tite.boinet.

Usage

## S3 method for class 'tite.boinet'
print(x, ...)
## S3 method for class 'tite.boinet'
print(x, ...)

Arguments

`x`	Object from `tite.boinet`.
`...`	More options to pass to print.

Value

No return values. Key summary results from tite.boinet are displayed with trial design settings.

Print tite.gboinet

Description

Display key summary results from tite.gboinet.

Usage

## S3 method for class 'tite.gboinet'
print(x, ...)
## S3 method for class 'tite.gboinet'
print(x, ...)

Arguments

`x`	Object from `tite.gboinet`.
`...`	More options to pass to print.

Value

No return values. Key summary results from tite.gboinet are displayed with trial design settings.

Conducting simulation study of TITE-BOIN-ET design

Description

Time-to-event Bayesian optimal interval design to accelerate dose-finding based on both efficacy and toxicity outcomes (TITE-BOIN-ET design) is implemented under a scenario specified. Operating characteristics of the design are summarized by the percentage of times that each dose level was selected as optimal biological dose and the average number of patients who were treated at each dose level.

Usage

tite.boinet(
  n.dose, start.dose, size.cohort, n.cohort,
  toxprob, effprob,
  phi=0.3, phi1=phi*0.1, phi2=phi*1.4, delta=0.6, delta1=delta*0.6,
  alpha.T1=0.5, alpha.E1=0.5, tau.T, tau.E,
  te.corr=0.2, gen.event.time="weibull",
  accrual, gen.enroll.time="uniform",
  stopping.npts=size.cohort*n.cohort,
  stopping.prob.T=0.95, stopping.prob.E=0.99,
  estpt.method, obd.method,
  w1= 0.33, w2=1.09,
  plow.ast=phi1, pupp.ast=phi2, qlow.ast=delta1/2, qupp.ast=delta,
  psi00=40, psi11=60,
  n.sim=1000, seed.sim=100)
tite.boinet(
  n.dose, start.dose, size.cohort, n.cohort,
  toxprob, effprob,
  phi=0.3, phi1=phi*0.1, phi2=phi*1.4, delta=0.6, delta1=delta*0.6,
  alpha.T1=0.5, alpha.E1=0.5, tau.T, tau.E,
  te.corr=0.2, gen.event.time="weibull",
  accrual, gen.enroll.time="uniform",
  stopping.npts=size.cohort*n.cohort,
  stopping.prob.T=0.95, stopping.prob.E=0.99,
  estpt.method, obd.method,
  w1= 0.33, w2=1.09,
  plow.ast=phi1, pupp.ast=phi2, qlow.ast=delta1/2, qupp.ast=delta,
  psi00=40, psi11=60,
  n.sim=1000, seed.sim=100)

Arguments

`n.dose`	Number of dose.
`start.dose`	Starting dose. The lowest dose is generally recommended.
`size.cohort`	Cohort size.
`n.cohort`	Number of cohort.
`toxprob`	Vector of true toxicity probability.
`effprob`	Vector of true efficacy probability.
`phi`	Target toxicity probability. The default value is `phi=0.3`.
`phi1`	Highest toxicity probability that is deemed sub-therapeutic such that dose-escalation should be pursued. The default value is `phi1=phi*0.1`.
`phi2`	Lowest toxicity probability that is deemed overly toxic such that dose de-escalation is needed. The default value is `phi2=phi*1.4`.
`delta`	Target efficacy probability. The default value is `delta=0.6`.
`delta1`	Minimum probability deemed efficacious such that the dose levels with less than delta1 are considered sub-therapeutic. The default value is `delta1=delta*0.6`.
`alpha.T1`	Probability that toxicity event occurs in the late half of toxicity assessment window. The default value is `alpha.T1=0.5`.
`alpha.E1`	Probability that efficacy event occurs in the late half of assessment window. The default value is `alpha.E1=0.5`.
`tau.T`	Toxicity assessment windows (days).
`tau.E`	Efficacy assessment windows (days).
`te.corr`	Correlation between toxicity and efficacy probability, specified as Gaussian copula parameter. The default value is `te.corr=0.2`.
`gen.event.time`	Method to generate the time to first toxicity and efficacy outcome. Weibull distribution is used when `gen.event.time="weibull"`. Uniform distribution is used when `gen.event.time="uniform"`. The default value is `gen.event.time="weibull"`.
`accrual`	Accrual rate (days) (average number of days necessary to enroll one patient).
`gen.enroll.time`	Method to generate enrollment time. Uniform distribution is used when `gen.enroll.time="uniform"`. Exponential distribution is used when `gen.enroll.time="exponential"`. The default value is `gen.enroll.time="uniform"`.
`stopping.npts`	Early study termination criteria for the number of patients. If the number of patients at the current dose reaches this criteria, the study is terminated. The default value is `stopping.npts=size.cohort*n.cohort`.
`stopping.prob.T`	Early study termination criteria for toxicity, taking a value between 0 and 1. If the posterior probability that toxicity outcome is less than the target toxicity probability (`phi`) is larger than this criteria, the dose levels are eliminated from the study. The default value is `stopping.prob.T=0.95`.
`stopping.prob.E`	Early study termination criteria for efficacy, taking a value between 0 and 1. If the posterior probability that efficacy outcome is less than the minimum efficacy probability (`delta1`) is larger than this criteria, the dose levels are eliminated from the study. The default value is `stopping.prob.E=0.99`.
`estpt.method`	Method to estimate the efficacy probability. Fractional polynomial logistic regression is used when `estpt.method="fp.logistic"`. Model averaging of multiple unimodal isotopic regression is used when `estpt.method="multi.iso"`. Observed efficacy probability is used when `estpt.method="obs.prob"`.
`obd.method`	Method to select the optimal biological dose. Utility defined by weighted function is used when `obd.method="utility.weighted"`. Utility defined by truncated linear function is used when `obd.method="utility.truncated.linear"`. Utility defined by scoring is used when `obd.method="utility.scoring"`. Highest estimated efficacy probability is used when `obd.method="max.effprob"`.
`w1`	Weight for toxicity-efficacy trade-off in utility defined by weighted function. This must be specified when using `obd.method="utility.weighted"`. The default value is `w1=0.33`.
`w2`	Weight for penalty imposed on toxic doses in utility defined by weighted function. This must be specified when using `obd.method="utility.weighted"`. The default value is `w2=1.09`.
`plow.ast`	Lower threshold of toxicity linear truncated function. This must be specified when using `obd.method="utility.truncated.linear"`. The default value is `plow.ast=phi1`.
`pupp.ast`	Upper threshold of toxicity linear truncated function. This must be specified when using `obd.method="utility.truncated.linear"`. The default value is `pupp.ast=phi2`.
`qlow.ast`	Lower threshold of efficacy linear truncated function. This must be specified when using `obd.method="utility.truncated.linear"`. The default value is `qlow.ast=delta1/2`.
`qupp.ast`	Upper threshold of efficacy linear truncated function. This must be specified when using `obd.method="utility.truncated.linear"`. The default value is `qupp.ast=delta`.
`psi00`	Score for toxicity=no and efficacy=no in utility defined by scoring. This must be specified when using `obd.method="utility.scoring"`. The default value is `psi00=40`.
`psi11`	Score for toxicity=yes and efficacy=yes in utility defined by scoring. This must be specified when using `obd.method="utility.scoring"`. The default value is `psi11=60`.
`n.sim`	Number of simulated trial. The default value is `n.sim=1000`.
`seed.sim`	Seed for random number generator. The default value is `seed.sim=100`.

Details

The tite.boinet is a function which generates the operating characteristics of the time-to-event Bayesian optimal interval design to accelerate dose-finding based on both efficacy and toxicity outcomes (TITE-BOIN-ET design) by a simulation study. Users can specify a variety of study settings to simulate studies, and choose methods to estimate the efficacy probability and to select the optimal biological dose. The operating characteristics of the design are summarized by the percentage of times that each dose level was selected as optimal biological dose and the average number of patients who were treated at each dose level. The percentage of times that the study was terminated and the expected study duration are also provided.

Value

The tite.boinet returns a list containing the following components:

`toxprob`	True toxicity probability.
`effprob`	True efficacy probability.
`phi`	Target toxicity probability.
`delta`	Target efficacy probability.
`lambda1`	Lower toxicity boundary in dose escalation/de-escalation.
`lambda2`	Upper toxicity boundary in dose escalation/de-escalation.
`eta1`	Lower efficacy boundary in dose escalation/de-escalation.
`tau.T`	Toxicity assessment windows (days).
`tau.E`	Efficacy assessment windows (days).
`accrual`	Accrual rate (days) (average number of days necessary to enroll one patient).
`estpt.method`	Method to estimate the efficacy probability.
`obd.method`	Method to select the optimal biological dose.
`n.patient`	Average number of patients who were treated at each dose level
`prop.select`	Percentage of times that each dose level was selected as optimal biological dose.
`prop.stop`	Percentage of times that the study was terminated and optimal biological dose was not selected.
`duration`	Expected study duration (days)

References

Takeda K, Morita S, Taguri M. TITE-BOIN-ET: Time-to-event Bayesian optimal interval design to accelerate dose-finding based on both efficacy and toxicity outcomes. Pharmaceutical Statistics 2020; 19(3):335-349.

Examples

n.dose      <- 6
start.dose  <- 1
size.cohort <- 3
n.cohort    <- 12

toxprob <- c(0.01,0.03,0.06,0.12,0.18,0.30)
effprob <- c(0.06,0.08,0.15,0.25,0.40,0.80)

phi   <- 0.33
delta <- 0.70

tau.T   <- 30
tau.E   <- 45
accrual <- 10

estpt.method <- "obs.prob"
obd.method   <- "max.effprob"

n.sim <- 10

tite.boinet(
  n.dose=n.dose, start.dose=start.dose,
  size.cohort=size.cohort, n.cohort=n.cohort,
  toxprob=toxprob, effprob=effprob,
  phi=phi, delta=delta,
  tau.T=tau.T, tau.E=tau.E, accrual=accrual,
  estpt.method=estpt.method, obd.method=obd.method,
  n.sim=n.sim)
n.dose      <- 6
start.dose  <- 1
size.cohort <- 3
n.cohort    <- 12

toxprob <- c(0.01,0.03,0.06,0.12,0.18,0.30)
effprob <- c(0.06,0.08,0.15,0.25,0.40,0.80)

phi   <- 0.33
delta <- 0.70

tau.T   <- 30
tau.E   <- 45
accrual <- 10

estpt.method <- "obs.prob"
obd.method   <- "max.effprob"

n.sim <- 10

tite.boinet(
  n.dose=n.dose, start.dose=start.dose,
  size.cohort=size.cohort, n.cohort=n.cohort,
  toxprob=toxprob, effprob=effprob,
  phi=phi, delta=delta,
  tau.T=tau.T, tau.E=tau.E, accrual=accrual,
  estpt.method=estpt.method, obd.method=obd.method,
  n.sim=n.sim)

Conducting simulation study of TITE-gBOIN-ET design

Description

Time-to-event generalized Bayesian optimal interval design to accelerate dose-finding accounting for ordinal graded efficacy and toxicity outcomes (TITE-gBOIN-ET design) is implemented under a scenario specified. Operating characteristics of the design are summarized by the percentage of times that each dose level was selected as optimal biological dose and the average number of patients who were treated at each dose level.

Usage

tite.gboinet(
  n.dose, start.dose, size.cohort, n.cohort,
  toxprob, effprob, sev.weight, res.weight,
  phi, phi1=phi*0.1, phi2=phi*1.4, delta, delta1=delta*0.6,
  alpha.T1=0.5, alpha.E1=0.5, tau.T, tau.E,
  te.corr=0.2, gen.event.time="weibull",
  accrual, gen.enroll.time="uniform",
  stopping.npts=size.cohort*n.cohort,
  stopping.prob.T=0.95, stopping.prob.E=0.99,
  estpt.method, obd.method,
  w1=0.33, w2=1.09,
  plow.ast=phi1, pupp.ast=phi2, qlow.ast=delta1/2, qupp.ast=delta,
  psi00=40, psi11=60,
  n.sim=1000, seed.sim=100)
tite.gboinet(
  n.dose, start.dose, size.cohort, n.cohort,
  toxprob, effprob, sev.weight, res.weight,
  phi, phi1=phi*0.1, phi2=phi*1.4, delta, delta1=delta*0.6,
  alpha.T1=0.5, alpha.E1=0.5, tau.T, tau.E,
  te.corr=0.2, gen.event.time="weibull",
  accrual, gen.enroll.time="uniform",
  stopping.npts=size.cohort*n.cohort,
  stopping.prob.T=0.95, stopping.prob.E=0.99,
  estpt.method, obd.method,
  w1=0.33, w2=1.09,
  plow.ast=phi1, pupp.ast=phi2, qlow.ast=delta1/2, qupp.ast=delta,
  psi00=40, psi11=60,
  n.sim=1000, seed.sim=100)

Arguments

`n.dose`	Number of dose.
`start.dose`	Starting dose. The lowest dose is generally recommended.
`size.cohort`	Cohort size.
`n.cohort`	Number of cohort.
`toxprob`	Vector of true toxicity probability.
`effprob`	Vector of true efficacy probability.
`sev.weight`	Vector of weight for toxicity category.
`res.weight`	Vector of weight for efficacy category.
`phi`	Target toxicity probability.
`phi1`	Highest toxicity probability that is deemed sub-therapeutic such that dose-escalation should be pursued. The default value is `phi1=phi*0.1`.
`phi2`	Lowest toxicity probability that is deemed overly toxic such that dose de-escalation is needed. The default value is `phi2=phi*1.4`.
`delta`	Target efficacy probability.
`delta1`	Minimum probability deemed efficacious such that the dose levels with less than delta1 are considered sub-therapeutic. The default value is `delta1=delta*0.6`.
`alpha.T1`	Probability that toxicity event occurs in the late half of toxicity assessment window. The default value is `alpha.T1=0.5`.
`alpha.E1`	Probability that efficacy event occurs in the late half of assessment window. The default value is `alpha.E1=0.5`.
`tau.T`	Toxicity assessment windows (days).
`tau.E`	Efficacy assessment windows (days).
`te.corr`	Correlation between toxicity and efficacy probability, specified as Gaussian copula parameter. The default value is `te.corr=0.2`.
`gen.event.time`	Method to generate the time to first toxicity and efficacy outcome. Weibull distribution is used when `gen.event.time="weibull"`. Uniform distribution is used when `gen.event.time="uniform"`. The default value is `gen.event.time="weibull"`.
`accrual`	Accrual rate (days) (average number of days necessary to enroll one patient).
`gen.enroll.time`	Method to generate enrollment time. Uniform distribution is used when `gen.enroll.time="uniform"`. Exponential distribution is used when `gen.enroll.time="exponential"`. The default value is `gen.enroll.time="uniform"`.
`stopping.npts`	Early study termination criteria for the number of patients. If the number of patients at the current dose reaches this criteria, the study is terminated. The default value is `stopping.npts=size.cohort*n.cohort`.
`stopping.prob.T`	Early study termination criteria for toxicity, taking a value between 0 and 1. If the posterior probability that toxicity outcome is less than the target toxicity probability (`phi`) is larger than this criteria, the dose levels are eliminated from the study. The default value is `stopping.prob.T=0.95`.
`stopping.prob.E`	Early study termination criteria for efficacy, taking a value between 0 and 1. If the posterior probability that efficacy outcome is less than the minimum efficacy probability (`delta1`) is larger than this criteria, the dose levels are eliminated from the study. The default value is `stopping.prob.E=0.99`.
`estpt.method`	Method to estimate the efficacy probability. Fractional polynomial logistic regression is used when `estpt.method="fp.logistic"`. Model averaging of multiple unimodal isotopic regression is used when `estpt.method="multi.iso"`. Observed efficacy probability is used when `estpt.method="obs.prob"`.
`obd.method`	Method to select the optimal biological dose. Utility defined by weighted function is used when `obd.method="utility.weighted"`. Utility defined by truncated linear function is used when `obd.method="utility.truncated.linear"`. Utility defined by scoring is used when `obd.method="utility.scoring"`. Highest estimated efficacy probability is used when `obd.method="max.effprob"`.
`w1`	Weight for toxicity-efficacy trade-off in utility defined by weighted function. This must be specified when using `obd.method="utility.weighted"`. The default value is `w1=0.33`.
`w2`	Weight for penalty imposed on toxic doses in utility defined by weighted function. This must be specified when using `obd.method="utility.weighted"`. The default value is `w2=1.09`.
`plow.ast`	Lower threshold of toxicity linear truncated function. This must be specified when using `obd.method="utility.truncated.linear"`. The default value is `plow.ast=phi1`.
`pupp.ast`	Upper threshold of toxicity linear truncated function. This must be specified when using `obd.method="utility.truncated.linear"`. The default value is `pupp.ast=phi2`.
`qlow.ast`	Lower threshold of efficacy linear truncated function. This must be specified when using `obd.method="utility.truncated.linear"`. The default value is `qlow.ast=delta1/2`.
`qupp.ast`	Upper threshold of efficacy linear truncated function. This must be specified when using `obd.method="utility.truncated.linear"`. The default value is `qupp.ast=delta`.
`psi00`	Score for toxicity=no and efficacy=no in utility defined by scoring. This must be specified when using `obd.method="utility.scoring"`. The default value is `psi00=40`.
`psi11`	Score for toxicity=yes and efficacy=yes in utility defined by scoring. This must be specified when using `obd.method="utility.scoring"`. The default value is `psi11=60`.
`n.sim`	Number of simulated trial. The default value is `n.sim=1000`.
`seed.sim`	Seed for random number generator. The default value is `seed.sim=100`.

Details

The tite.gboinet is a function which generates the operating characteristics of the time-to-event generalized Bayesian optimal interval design to accelerate dose-finding accounting for ordinal graded efficacy and toxicity outcomes (TITE-gBOIN-ET design) by a simulation study. Users can specify a variety of study settings to simulate studies, and choose methods to estimate the efficacy probability and to select the optimal biological dose. The operating characteristics of the design are summarized by the percentage of times that each dose level was selected as optimal biological dose and the average number of patients who were treated at each dose level. The percentage of times that the study was terminated and the expected study duration are also provided.

Value

The tite.gboinet returns a list containing the following components:

`toxprob`	True toxicity probability.
`effprob`	True efficacy probability.
`nETS`	Normalized equivalent toxicity score.
`nEES`	Normalized equivalent efficacy score.
`phi`	Target toxicity probability.
`delta`	Target efficacy probability.
`lambda1`	Lower toxicity boundary in dose escalation/de-escalation.
`lambda2`	Upper toxicity boundary in dose escalation/de-escalation.
`eta1`	Lower efficacy boundary in dose escalation/de-escalation.
`tau.T`	Toxicity assessment windows (days).
`tau.E`	Efficacy assessment windows (days).
`accrual`	Accrual rate (days) (average number of days necessary to enroll one patient).
`estpt.method`	Method to estimate the efficacy probability.
`obd.method`	Method to select the optimal biological dose.
`n.patient`	Average number of patients who were treated at each dose level
`prop.select`	Percentage of times that each dose level was selected as optimal biological dose.
`prop.stop`	Percentage of times that the study was terminated and optimal biological dose was not selected.
`duration`	Expected study duration (days)

References

Takeda K, Yamaguchi Y, Taguri M, Morita S. TITE-gBOIN-ET: Time-to-event generalized Bayesian optimal interval design to accelerate dose-finding accounting for ordinal graded efficacy and toxicity outcomes. Biometrical Journal 2023 (in press).

Examples

n.dose      <- 6
start.dose  <- 1
size.cohort <- 3
n.cohort    <- 12

toxprob <- rbind(c(0.94,0.87,0.79,0.68,0.62,0.50),
                 c(0.05,0.10,0.15,0.20,0.20,0.20),
                 c(0.01,0.03,0.05,0.10,0.15,0.25),
                 c(0.00,0.00,0.01,0.02,0.03,0.05))
effprob <- rbind(c(0.64,0.52,0.45,0.35,0.20,0.05),
                 c(0.30,0.40,0.40,0.40,0.40,0.15),
                 c(0.05,0.05,0.10,0.15,0.20,0.35),
                 c(0.01,0.03,0.05,0.10,0.20,0.45))

sev.weight <- c(0.00,0.50,1.00,1.50)
res.weight <- c(0.00,0.25,1.00,3.00)

phi   <- 0.33
delta <- 0.70

tau.T   <- 30
tau.E   <- 45
accrual <- 10

estpt.method <- "obs.prob"
obd.method   <- "max.effprob"

n.sim <- 10

tite.gboinet(
  n.dose=n.dose, start.dose=start.dose,
  size.cohort=size.cohort, n.cohort=n.cohort,
  toxprob=toxprob, effprob=effprob,
  sev.weight=sev.weight, res.weight=res.weight,
  phi=phi, delta=delta,
  tau.T=tau.T, tau.E=tau.E, accrual=accrual,
  estpt.method=estpt.method, obd.method=obd.method,
  n.sim=n.sim)
n.dose      <- 6
start.dose  <- 1
size.cohort <- 3
n.cohort    <- 12

toxprob <- rbind(c(0.94,0.87,0.79,0.68,0.62,0.50),
                 c(0.05,0.10,0.15,0.20,0.20,0.20),
                 c(0.01,0.03,0.05,0.10,0.15,0.25),
                 c(0.00,0.00,0.01,0.02,0.03,0.05))
effprob <- rbind(c(0.64,0.52,0.45,0.35,0.20,0.05),
                 c(0.30,0.40,0.40,0.40,0.40,0.15),
                 c(0.05,0.05,0.10,0.15,0.20,0.35),
                 c(0.01,0.03,0.05,0.10,0.20,0.45))

sev.weight <- c(0.00,0.50,1.00,1.50)
res.weight <- c(0.00,0.25,1.00,3.00)

phi   <- 0.33
delta <- 0.70

tau.T   <- 30
tau.E   <- 45
accrual <- 10

estpt.method <- "obs.prob"
obd.method   <- "max.effprob"

n.sim <- 10

tite.gboinet(
  n.dose=n.dose, start.dose=start.dose,
  size.cohort=size.cohort, n.cohort=n.cohort,
  toxprob=toxprob, effprob=effprob,
  sev.weight=sev.weight, res.weight=res.weight,
  phi=phi, delta=delta,
  tau.T=tau.T, tau.E=tau.E, accrual=accrual,
  estpt.method=estpt.method, obd.method=obd.method,
  n.sim=n.sim)

Utility defined by scoring

Description

Given estimated toxicity and efficacy probabilities, the utility which is defined by scoring is calculated.

Usage

utility.scoring(probt, probe, psi00, psi11)
utility.scoring(probt, probe, psi00, psi11)

Arguments

`probt`	Estimated toxicity probability.
`probe`	Estimated efficacy probability.
`psi00`	Score for toxicity=no and efficacy=no.
`psi11`	Score for toxicity=yes and efficacy=yes.

Value

The utility.scoring returns a utility value defined by the scoring.

Utility defined by truncated linear function

Description

Given estimated toxicity and efficacy probabilities, the utility which is defined by truncated linear functions is Calculated.

Usage

utility.truncated.linear(probt, probe, tlow, tupp, elow, eupp)
utility.truncated.linear(probt, probe, tlow, tupp, elow, eupp)

Arguments

`probt`	Estimated toxicity probability
`probe`	Estimated efficacy probability
`tlow`	Lower threshold of toxicity linear truncated function.
`tupp`	Upper threshold of toxicity linear truncated function.
`elow`	Lower threshold of efficacy linear truncated function.
`eupp`	Upper threshold of efficacy linear truncated function.

Value

The utility.truncated.linear returns a utility value defined by the truncated linear functions.

Utility defined by weighted function

Description

Given estimated toxicity and efficacy probabilities, the utility which is defined by a weighted function is Calculated.

Usage

utility.weighted(probt, probe, w1, w2, tox.upper)
utility.weighted(probt, probe, w1, w2, tox.upper)

Arguments

`probt`	Estimated toxicity probability.
`probe`	Estimated efficacy probability.
`w1`	Weight for toxicity-efficacy trade-off.
`w2`	Weight for penalty imposed on toxic doses.
`tox.upper`	Upper bound of toxicity probability.

Value

The utility.weighted returns a utility value defined by the weighted function.

Package 'boinet'

Help Index

Conducting simulation study of BOIN-ET design

Description

Usage

Arguments

Details

Value

References

Examples

Fractional polynomial logistic regression

Description

Usage

Arguments

Value

Conducting simulation study of gBOIN-ET design

Description

Usage

Arguments

Details

Value

References

Examples

Grid search to find optimal threshold values of toxicity and efficacy interval

Description

Usage

Arguments

Value

Examples

Model averaging of multiple unimodal isotopic regression

Description

Usage

Arguments

Value

Optimal biological dose selection

Description

Usage

Arguments

Value

Print boinet

Description

Usage

Arguments

Value

See Also

Print gboinet

Description

Usage

Arguments

Value

See Also

Print tite.boinet

Description

Usage

Arguments

Value

See Also

Print tite.gboinet

Description

Usage

Arguments

Value

See Also

Conducting simulation study of TITE-BOIN-ET design

Description

Usage

Arguments

Details

Value

References

Examples

Conducting simulation study of TITE-gBOIN-ET design

Description

Usage

Arguments

Details

Value

References

Examples

Utility defined by scoring