Package 'SubgrpID'

Title: Patient Subgroup Identification for Clinical Drug Development
Description: Implementation of Sequential BATTing (bootstrapping and aggregating of thresholds from trees) for developing threshold-based multivariate (prognostic/predictive) biomarker signatures. Variable selection is automatically built-in. Final signatures are returned with interaction plots for predictive signatures. Cross-validation performance evaluation and testing dataset results are also output. Detail algorithms are described in Huang et al (2017) <doi:10.1002/sim.7236>.
Authors: Xin Huang [aut, cre, cph], Yan Sun [aut], Saptarshi Chatterjee [aut], Paul Trow [aut]
Maintainer: Xin Huang <[email protected]>
License: GPL (>= 2)
Version: 0.12
Built: 2024-10-31 06:26:16 UTC
Source: CRAN

Help Index

balanced.folds

Description

Create balanced folds for cross-validation.

Usage

balanced.folds(y, nfolds = min(min(table(y)), 10))

Arguments

y

the response vector
nfolds

number of folds

Details

Create balanced folds for cross-validation.

Value

This function returns balanced folds

batting.pred

Description

Main predictive BATTing function

Usage

batting.pred(
  dataset,
  ids,
  yvar,
  censorvar,
  trtvar,
  type,
  class.wt,
  xvar,
  n.boot,
  des.res,
  min.sigp.prcnt
)

Arguments

dataset

input dataset in data frame
ids

training indices
yvar

response variable name
censorvar

censoring variable name 1:event; 0: censor.
trtvar

treatment variable name
type

"c" continuous; "s" survival; "b" binary
class.wt

vector of length 2 used to weight the accuracy score , useful when there is class imbalance in binary data defaults to c(1,1)
xvar

name of predictor for which cutpoint needs to be obtained
n.boot

number of bootstraps for BATTing step.
des.res

the desired response. "larger": prefer larger response. "smaller": prefer smaller response.
min.sigp.prcnt

desired proportion of signature positive group size for a given cutoff.

Details

Main predictive BATTing function

Value

a signature rule consisting of variable name, direction, optimal cutpoint and the corresponding p-value.

batting.prog

Description

Main prognostic BATTing function

Usage

batting.prog(
  dataset,
  ids,
  yvar,
  censorvar,
  type,
  class.wt,
  xvar,
  n.boot,
  des.res,
  min.sigp.prcnt
)

Arguments

dataset

input dataset in data frame
ids

training indices
yvar

response variable name
censorvar

censoring variable name 1:event; 0: censor.
type

"c" continuous; "s" survival; "b" binary
class.wt

vector of length 2 used to weight the accuracy score , useful when there is class imbalance in binary data defaults to c(1,1)
xvar

name of predictor for which cutpoint needs to be obtained
n.boot

number of bootstraps for BATTing step.
des.res

the desired response. "larger": prefer larger response. "smaller": prefer smaller response.
min.sigp.prcnt

desired proportion of signature positive group size for a given cutoff.

Details

Main prognostic BATTing function

Value

a signature rule consisting of variable name, direction, optimal cutpoint and the corresponding p-value.

binary.stats

Description

A function for binary statistics

Usage

binary.stats(pred.class, y.vec)

Arguments

pred.class

predicted output for each subject
y.vec

response vector

Details

A function for binary statistics

Value

a data frame with sensitivity, specificity, NPV, PPV and accuracy

cv.folds

Description

Cross-validation folds.

Usage

cv.folds(n, folds = 10)

Arguments

n

number of observations.
folds

number of folds.

Details

Cross-validation folds.

Value

a list containing the observation numbers for each fold.

cv.pval

Description

p-value calculation for each iteration of cross validation.

Usage

cv.pval(yvar, censorvar = NULL, trtvar = NULL, data, type = "s")

Arguments

yvar

response variable name.
censorvar

censor-variable name.
trtvar

treatment variable name. For prognostic case trtvar=NULL.
data

dataset containing response and predicted output.
type

data type - "c" - continuous , "b" - binary, "s" - time to event - default = "c".

Details

p-value calculation for each iteration of cross validation.

Value

p-value based on response and prediction vector for each iteration.

cv.seqlr.batting

Description

Cross Validation for Sequential BATTing

Usage

cv.seqlr.batting(
  y,
  x,
  censor.vec = NULL,
  trt.vec = NULL,
  trtref = NULL,
  type = "c",
  n.boot = 50,
  des.res = "larger",
  class.wt = c(1, 1),
  min.sigp.prcnt = 0.2,
  pre.filter = NULL,
  filter.method = NULL,
  k.fold = 5,
  cv.iter = 50,
  max.iter = 500
)

Arguments

y

data frame containing the response
x

data frame containing the predictors
censor.vec

vector giving the censor status (only for TTE data , censor=0,event=1) : default = NULL
trt.vec

vector containing values of treatment variable ( for predictive signature). Set trt.vec to NULL for prognostic signature.
trtref

code for treatment arm.
type

data type. "c" - continuous , "b" - binary, "s" - time to event : default = "c".
n.boot

number of bootstraps in BATTing step.
des.res

the desired response. "larger": prefer larger response. "smaller": prefer smaller response
class.wt

vector of length 2 used to weight the accuracy score , useful when there is class imbalance in binary data defaults to c(1,1)
min.sigp.prcnt

desired proportion of signature positive group size for a given cutoff.
pre.filter

NULL, no prefiltering conducted;"opt", optimized number of predictors selected; An integer: min(opt, integer) of predictors selected.
filter.method

NULL, no prefiltering, "univariate", univaraite filtering; "glmnet", glmnet filtering, "unicart": univariate rpart filtering for prognostic case.
k.fold

number of folds for CV.
cv.iter

algorithm terminates after cv.iter successful iterations of cross-validation.
max.iter

total number of iterations allowed (including unsuccessful ones).

Details

Cross Validation for Sequential BATTing

Value

a list containing with following entries:

stats.summary

Summary of performance statistics.

pred.classes

Data frame containing the predictive clases (TRUE/FALSE) for each iteration.

folds

Data frame containing the fold indices (index of the fold for each row) for each iteration.

sig.list

List of length cv.iter * k.fold containing the signature generated at each of the k folds, for all iterations.

error.log

List of any error messages that are returned at an iteration.

interplot

Treatment*subgroup interaction plot for predictive case

data.gen

Description

Function for simulated data generation

Usage

data.gen(
  n,
  k,
  prevalence = sqrt(0.5),
  prog.eff = 1,
  sig2,
  y.sig2,
  rho,
  rhos.bt.real,
  a.constent
)

Arguments

n

Total sample size
k

Number of markers
prevalence

prevalence of predictive biomarkers with values above the cutoff
prog.eff

effect size betabeta for prognostic biomarker
sig2

standard deviation of each marker
y.sig2

Standard Deviation of the error term in the linear component
rho

rho*sig2 is the entries for covariance matrix between pairs of different k markers
rhos.bt.real

correlation between each prognostic and predictive markers
a.constent

a constant is set such that there is no overall treatment effect

Details

Function for simulated data generation

Value

A list of simulated clinical trial data with heterogeneous prognostic and predictive biomarkers

Examples

n <- 500
k <- 10
prevalence <- sqrt(0.5)
rho<-0.2
sig2 <- 2
rhos.bt.real <- c(0, rep(0.1, (k-3)))*sig2
y.sig2 <- 1
prog.eff <- 0.5
effect.size <- 1
a.constent <- effect.size/(2*(1-prevalence))
ObsData <- data.gen(n=n, k=k, prevalence=prevalence, prog.eff=prog.eff,
                    sig2=sig2, y.sig2=y.sig2, rho=rho,
                    rhos.bt.real=rhos.bt.real, a.constent=a.constent)

evaluate.cv.results

Description

Take the raw output of kfold.cv and calculate performance statistics for each iteration of the cross-validation.

Usage

evaluate.cv.results(cv.data, y, censor.vec, trt.vec, type)

Arguments

cv.data

output of prediction function from kfold.cv
y

data frame of the response variable from CV data.
censor.vec

data frame indicating censoring for survival data. For binary or continuous data, set censor.vec <- NULL.
trt.vec

data frame indicating whether or not the patient was treated. For the pronostic case, set trt.vec <- NULL.
type

data type - "c" - continuous , "b" - binary, "s" - time to event - default = "c"

Details

Cross-validation Performance Evaluation

Value

a list containing raw statistics and fold information

evaluate.results

Description

Get statistics for a single set of predictions.

Usage

evaluate.results(
  y,
  predict.data,
  censor.vec = NULL,
  trt.vec = NULL,
  trtref = NULL,
  type
)

Arguments

y

data frame of the response variable.
predict.data

output of prediction function from kfold.cv.
censor.vec

data frame indicating censoring for survival data. For binary or continuous data, set censor.vec <- NULL.
trt.vec

data frame indicating whether or not the patient was treated. For the pronostic case, set trt.vec <- NULL.
trtref

treatment reference.
type

data type - "c" - continuous , "b" - binary, "s" - time to event - default = "c".

Details

Get statistics for a single set of predictions.

Value

a list containing p-value and group statistics.

filter

Description

Filter function for Prognostic and preditive biomarker signature development for Exploratory Subgroup Identification in Randomized Clinical Trials

Usage

filter(
  data,
  type = "c",
  yvar,
  xvars,
  censorvar = NULL,
  trtvar = NULL,
  trtref = 1,
  n.boot = 50,
  cv.iter = 20,
  pre.filter = length(xvars),
  filter.method = NULL
)

Arguments

data

input data frame
type

type of response variable: "c" continuous; "s" survival; "b" binary
yvar

variable (column) name for response variable
xvars

vector of variable names for predictors (covariates)
censorvar

variable name for censoring (1: event; 0: censor), default = NULL
trtvar

variable name for treatment variable, default = NULL (prognostic signature)
trtref

coding (in the column of trtvar) for treatment arm, default = 1 (no use for prognostic signature)
n.boot

number of bootstrap for the BATTing procedure
cv.iter

Algotithm terminates after cv.iter successful iterations of cross-validation, or after max.iter total iterations, whichever occurs first
pre.filter

NULL (default), no prefiltering conducted;"opt", optimized number of predictors selected; An integer: min(opt, integer) of predictors selected
filter.method

NULL (default), no prefiltering; "univariate", univaraite filtering; "glmnet", glmnet filtering

Details

Filter function for predictive/prognostic biomarker candidates for signature development

The function contains two algorithms for filtering high-dimentional multivariate (prognostic/predictive) biomarker candidates via univariate fitering (used p-values of group difference for prognostic case, p-values of interaction term for predictive case); LASSO/Elastic Net method. (Tian L. et al 2012)

Value

var

a vector of filter results of variable names

References

Tian L, Alizadeh A, Gentles A, Tibshirani R (2012) A Simple Method for Detecting Interactions between a Treatment and a Large Number of Covariates. J Am Stat Assoc. 2014 Oct; 109(508): 1517-1532.

Examples

# no run

filter.glmnet

Description

Flitering using MC glmnet

Usage

filter.glmnet(
  data,
  type,
  yvar,
  xvars,
  censorvar,
  trtvar,
  trtref,
  n.boot = 50,
  cv.iter = 20,
  pre.filter = length(xvars)
)

Arguments

data

input data frame
type

"c" continuous; "s" survival; "b" binary
yvar

response variable name
xvars

covariates variable name
censorvar

censoring variable name 1:event; 0: censor.
trtvar

treatment variable name
trtref

code for treatment arm
n.boot

number of bootstrap for filtering
cv.iter

number of iterations required for MC glmnet filtering
pre.filter

NULL, no prefiltering conducted;"opt", optimized number of predictors selected; An integer: min(opt, integer) of predictors selected

Details

Flitering using MC glmnet

Value

variables selected after glmnet filtering

filter.unicart

Description

rpart filtering

Usage

filter.unicart(
  data,
  type,
  yvar,
  xvars,
  censorvar,
  trtvar,
  trtref = 1,
  pre.filter = length(xvars)
)

Arguments

data

input data frame
type

"c" continuous; "s" survival; "b" binary
yvar

response variable name
xvars

covariates variable name
censorvar

censoring variable name 1:event; 0: censor.
trtvar

treatment variable name
trtref

code for treatment arm
pre.filter

NULL, no prefiltering conducted;"opt", optimized number of predictors selected; An integer: min(opt, integer) of predictors selected

Details

rpart filtering (only for prognostic case)

Value

selected covariates after rpart filtering

filter.univariate

Description

Univariate Filtering

Usage

filter.univariate(
  data,
  type,
  yvar,
  xvars,
  censorvar,
  trtvar,
  trtref = 1,
  pre.filter = length(xvars)
)

Arguments

data

input data frame
type

"c" continuous; "s" survival; "b" binary
yvar

response variable name
xvars

covariates variable name
censorvar

censoring variable name 1:event; 0: censor.
trtvar

treatment variable name
trtref

code for treatment arm
pre.filter

NULL, no prefiltering conducted;"opt", optimized number of predictors selected; An integer: min(opt, integer) of predictors selected

Details

Univariate Filtering

Value

covariate names after univariate filtering.

find.pred.stats

Description

Find predictive stats from response and prediction vector

Usage

find.pred.stats(data, yvar, trtvar, type, censorvar)

Arguments

data

data frame with response and prediction vector
yvar

response variable name
trtvar

treatment variable name
type

data type - "c" - continuous , "b" - binary, "s" - time to event - default = "c".
censorvar

censoring variable name

Details

Find predictive stats from response and prediction vector

Value

a data frame of predictive statistics

find.prog.stats

Description

Find prognostic stats from response and prediction vector

Usage

find.prog.stats(data, yvar, type, censorvar)

Arguments

data

data frame with response and prediction vector
yvar

response variable name
type

data type - "c" - continuous , "b" - binary, "s" - time to event - default = "c".
censorvar

censoring variable name

Details

Find prognostic stats from response and prediction vector

Value

a data frame of predictive statistics

get.var.counts.seq

Description

Get signature variables from output of seqlr.batting.

Usage

get.var.counts.seq(sig.list, xvars)

Arguments

sig.list

signature list returned by seqlr.batting.
xvars

predictor variable names

Value

the variables included in signature rules returned by seqlr.batting

interaction.plot

Description

A function for interaction plot

Usage

interaction.plot(
  data.eval,
  type,
  main = "Interaction Plot",
  trt.lab = c("Trt.", "Ctrl.")
)

Arguments

data.eval

output of evaluate.results or summarize.cv.stats
type

data type - "c" - continuous , "b" - binary, "s" - time to event - default = "c".
main

title of the plot
trt.lab

treatment label

Details

A function for interaction plot

Value

A ggplot object.

kfold.cv

Description

Perform k-fold cross-validation of a model.

Usage

kfold.cv(
  data,
  model.Rfunc,
  model.Rfunc.args,
  predict.Rfunc,
  predict.Rfunc.args,
  k.fold = 5,
  cv.iter = 50,
  strata,
  max.iter = 500
)

Arguments

data

the CV data
model.Rfunc

Name of the model function.
model.Rfunc.args

List of input arguments to model.Rfunc.
predict.Rfunc

Name of the prediction function, which takes the prediction rule returned by model.Rfunc along with any input data (not necessarily the input data to kfold.cv) and returns a TRUE-FALSE predictionvector specifying the positive and negative classes for the data.
predict.Rfunc.args

List containing input arguments to predict.Rfunc, except for data and predict.rule.
k.fold

Number of folds of the cross-validation.
cv.iter

Number of iterations of the cross-validation. If model.Rfunc returns an error at any of the k.fold calls, the current iteration is aborted. Iterations are repeated until cv.iter successful iterations have occurred.
strata

Stratification vector of length the number of rows of data, usually corresponding to the vector of events.
max.iter

Function stops after max.iter iterations even if cv.iter successful iterations have not occurred.

Details

Perform k-fold cross-validation of a model.

Value

List of length 2 with the following fields:

cv.data - List of length cv.iter. Entry i contains the output of predict.Rfunc at the ith iteration.

sig.list - list of length cv.iter * k.fold, whose entries are the prediction.rules (signatures) returned by model.Rfunc at each k.fold iteration.

make.arg.list

Description

Create a list of variables corresponding to the arguments of the function func.name and assigns values.

Usage

make.arg.list(func.name)

Arguments

func.name

function name

Details

Create a list of variables corresponding to the arguments of the function func.name and assigns values.

Value

list of variables corresponding to the arguments of the function

permute.rows

Description

Randomly permute the rows of a matrix.

Usage

permute.rows(A)

Arguments

A

a matrix for which its rows have to be permuted.

Details

Randomly permute the rows of a matrix.

Value

the matrix with permuted rows.

permute.vector

Description

Randomly permute the entries of a vector.

Usage

permute.vector(x)

Arguments

x

the vector for which its entries have to be permuted

Details

Randomly permute the entries of a vector.

Value

the permuted vector

pred.seqlr

Description

Assign positive and negative groups based on predict.rule, the output of seqlr.batting.

Usage

pred.seqlr(x, predict.rule)

Arguments

x

input predictors matrix
predict.rule

Prediction rule returned by seqlr.batting.

Details

Prediction function for Sequential BATTing

Value

a logical vector indicating the prediction for each row of data.

pred.seqlr.cv

Description

Assign positive and negative groups for cross-validation data given prediction rule in predict.rule.

Usage

pred.seqlr.cv(data, predict.rule, args)

Arguments

data

input data frame
predict.rule

Prediction rule returned by seqlr.batting.
args

Prediction rule arguments

Details

Prediction function for CV Sequential BATTing

Value

a logical vector indicating the prediction for each row of data.

query.data

Description

internal function used in seqlr.batting

Usage

query.data(data, rule)

Arguments

data

the given dataset
rule

rule is a vector of the form [x-variable, direction, cutoff, p-value]

Details

internal function used in seqlr.batting

Value

a logical variable indicating whether rules are satisfied or not.

resample

Description

Creates a permutation of given size.

Usage

resample(x, size, ...)

Arguments

x

the x vector.
size

resampling size.
...

optional argument.

Details

Creates a permutation of given size.

Value

A resample of x is returned.

seqlr.batting

Description

Perform sequential BATTing method.

Usage

seqlr.batting(
  y,
  x,
  censor.vec = NULL,
  trt.vec = NULL,
  trtref = NULL,
  type = "c",
  n.boot = 50,
  des.res = "larger",
  class.wt = c(1, 1),
  min.sigp.prcnt = 0.2,
  pre.filter = NULL,
  filter.method = NULL
)

Arguments

y

data frame containing the response.
x

data frame containing the predictors.
censor.vec

vector containing the censor status (only for TTE data , censor=0,event=1) - default = NULL.
trt.vec

vector containing values of treatment variable ( for predictive signature). Set trt.vec to NULL for prognostic signature.
trtref

code for treatment arm.
type

data type. "c" - continuous , "b" - binary, "s" - time to event : default = "c".
n.boot

number of bootstraps in BATTing step.
des.res

the desired response. "larger": prefer larger response. "smaller": prefer smaller response
class.wt

vector of length 2 used to weight the accuracy score , useful when there is class imbalance in binary data defaults to c(1,1)
min.sigp.prcnt

desired proportion of signature positive group size for a given cutoff.
pre.filter

NULL, no prefiltering conducted;"opt", optimized number of predictors selected; An integer: min(opt, integer) of predictors selected
filter.method

NULL, no prefiltering, "univariate", univaraite filtering; "glmnet", glmnet filtering, "unicart": univariate rpart filtering for prognostic case.

Details

Perform sequential BATTing method.

Value

it returns a list of signature rules consisting of variable names, directions, thresholds and the loglikelihood at each step the signatures are applied.

seqlr.batting.wrapper

Description

Wrapper function for seqlr.batting, to be passed to kfold.cv.

Usage

seqlr.batting.wrapper(data, args)

Arguments

data

data frame equal to cbind(y, x, trt, censor), where y and x are inputs to seqlr.batting.
args

list containing all other input arguments to seq.batting except for x and y. Also contains xvars=names(x) and yvar=names(y).

Details

Wrapper function for seqlr.batting, to be passed to kfold.cv.

Value

prediction rule returned by seqlr.batting.

seqlr.find.cutoff.pred

Description

Find cutoff for predictive case.

Usage

seqlr.find.cutoff.pred(
  data,
  yvar,
  censorvar,
  xvar,
  trtvar,
  type,
  class.wt,
  dir,
  nsubj,
  min.sigp.prcnt
)

Arguments

data

input data frame.
yvar

response variable name.
censorvar

censoring variable name.
xvar

name of predictor for which cutpoint needs to be obtained.
trtvar

treatment variable name.
type

"c" continuous; "s" survival; "b" binary.
class.wt

vector of length 2 used to weight the accuracy score , useful when there is class imbalance in binary data defaults to c(1,1).
dir

direction of cut.
nsubj

number of subjects.
min.sigp.prcnt

desired proportion of signature positive group size for a given cutoff.

Details

Find cutoff for predictive case.

Value

the optimal score (p-value of subgroup*treatment interaction) for a predictor variable.

seqlr.find.cutoff.prog

Description

Find cutoff for prognostic case.

Usage

seqlr.find.cutoff.prog(
  data,
  yvar,
  censorvar,
  xvar,
  type,
  class.wt,
  dir,
  nsubj,
  min.sigp.prcnt
)

Arguments

data

input data frame.
yvar

response variable name.
censorvar

censoring variable name.
xvar

name of predictor for which cutpoint needs to be obtained.
type

"c" continuous; "s" survival; "b" binary.
class.wt

vector of length 2 used to weight the accuracy score , useful when there is class imbalance in binary data defaults to c(1,1).
dir

direction of cut.
nsubj

number of subjects.
min.sigp.prcnt

desired proportion of signature positive group size for a given cutoff.

Details

Find cutoff for prognostic case.

Value

the optimal score (p-value of main effect) for a predictor variable.

seqlr.score.pred

Description

Compute score of cutoff for predictive case

Usage

seqlr.score.pred(
  data,
  yvar,
  censorvar,
  xvar,
  trtvar,
  cutoff,
  type,
  class.wt,
  dir,
  nsubj,
  min.sigp.prcnt
)

Arguments

data

input data frame.
yvar

response variable name.
censorvar

censoring variable name.
xvar

name of predictor for which cutpoint needs to be obtained.
trtvar

treatment variable name.
cutoff

a specific cutpoint for which the score needs to be computed.
type

"c" continuous; "s" survival; "b" binary.
class.wt

vector of length 2 used to weight the accuracy score , useful when there is class imbalance in binary data defaults to c(1,1).
dir

direction of cut.
nsubj

number of subjects.
min.sigp.prcnt

desired proportion of signature positive group size for a given cutoff.

Details

Compute score of cutoff for predictive case

Value

score (p-value of treatment*subgroup interaction) for the given cutoff.

seqlr.score.prog

Description

Compute score of cutoff for prognostic case

Usage

seqlr.score.prog(
  data,
  yvar,
  censorvar,
  xvar,
  cutoff,
  type,
  class.wt,
  dir,
  nsubj,
  min.sigp.prcnt
)

Arguments

data

input data frame.
yvar

response variable name.
censorvar

censoring variable name.
xvar

name of predictor for which cutpoint needs to be obtained.
cutoff

a specific cutpoint for which the score needs to be computed.
type

"c" continuous; "s" survival; "b" binary.
class.wt

vector of length 2 used to weight the accuracy score , useful when there is class imbalance in binary data defaults to c(1,1).
dir

direction of cut.
nsubj

number of subjects.
min.sigp.prcnt

desired proportion of signature positive group size for a given cutoff.

Details

Compute score of cutoff for prognostic case

Value

score (p-value of main effect) for the given cutoff.

SubgrpID

Description

Exploratory Subgroup Identification main function

Usage

SubgrpID(
  data.train,
  data.test = NULL,
  yvar,
  censorvar = NULL,
  trtvar = NULL,
  trtref = NULL,
  xvars,
  type = "c",
  n.boot = 25,
  des.res = "larger",
  min.sigp.prcnt = 0.2,
  pre.filter = NULL,
  filter.method = NULL,
  k.fold = 5,
  cv.iter = 20,
  max.iter = 500,
  mc.iter = 20,
  method = c("Seq.BT"),
  do.cv = FALSE,
  out.file = NULL,
  file.path = "",
  plots = FALSE
)

Arguments

data.train

data frame for training dataset
data.test

data frame for testing dataset, default = NULL
yvar

variable (column) name for response variable
censorvar

variable name for censoring (1: event; 0: censor), default = NULL
trtvar

variable name for treatment variable, default = NULL (prognostic signature)
trtref

coding (in the column of trtvar) for treatment arm
xvars

vector of variable names for predictors (covariates)
type

type of response variable: "c" continuous; "s" survival; "b" binary
n.boot

number of bootstrap for batting procedure, or the variable selection procedure for PRIM; for PRIM, when n.boot=0, bootstrapping for variable selection is not conducted
des.res

the desired response. "larger": prefer larger response. "smaller": prefer smaller response
min.sigp.prcnt

desired proportion of signature positive group size for a given cutoff
pre.filter

NULL (default), no prefiltering conducted;"opt", optimized number of predictors selected; An integer: min(opt, integer) of predictors selected
filter.method

NULL (default), no prefiltering; "univariate", univaraite filtering; "glmnet", glmnet filtering; "unicart", univariate rpart filtering for prognostic case
k.fold

cross-validation folds
cv.iter

Algotithm terminates after cv.iter successful iterations of cross-validation, or after max.iter total iterations, whichever occurs first
max.iter

total iterations, whichever occurs first
mc.iter

number of iterations for the Monte Carlo procedure to get a stable "best number of predictors"
method

current version only supports sequential-BATTing ("Seq.BT") for subgroup identification
do.cv

whether to perform cross validation for performance evaluation. TRUE or FALSE (Default)
out.file

Name of output result files excluding method name. If NULL no output file would be saved
file.path

default: current working directory. When specifying a dir, use "/" at the end. e.g. "TEMP/"
plots

default: FALSE. whether to save plots

Details

Function for SubgrpID

Value

A list with SubgrpID output

res

list of all results from the algorithm

train.stat

list of subgroup statistics on training dataset

test.stat

list of subgroup statistics on testing dataset

cv.res

list of all results from cross-validation on training dataset

train.plot

interaction plot for training dataset

test.plot

interaction plot for testing dataset

Examples

# no run
n <- 40
k <- 5
prevalence <- sqrt(0.5)
rho<-0.2
sig2 <- 2
rhos.bt.real <- c(0, rep(0.1, (k-3)))*sig2
y.sig2 <- 1
yvar="y.binary"
xvars=paste("x", c(1:k), sep="")
trtvar="treatment"
prog.eff <- 0.5
effect.size <- 1
a.constent <- effect.size/(2*(1-prevalence))
set.seed(888)
ObsData <- data.gen(n=n, k=k, prevalence=prevalence, prog.eff=prog.eff,
                    sig2=sig2, y.sig2=y.sig2, rho=rho,
                    rhos.bt.real=rhos.bt.real, a.constent=a.constent)
TestData <- data.gen(n=n, k=k, prevalence=prevalence, prog.eff=prog.eff,
                     sig2=sig2, y.sig2=y.sig2, rho=rho,
                     rhos.bt.real=rhos.bt.real, a.constent=a.constent)
subgrp <- SubgrpID(data.train=ObsData$data,
                   data.test=TestData$data,
                   yvar=yvar,
                   trtvar=trtvar,
                   trtref="1",
                   xvars=xvars,
                   type="b",
                   n.boot=5, # suggest n.boot > 25, depends on sample size
                   des.res = "larger",
 #                 do.cv = TRUE,
 #                 cv.iter = 2, # uncomment to run CV
                   method="Seq.BT")
subgrp$res
subgrp$train.stat
subgrp$test.stat
subgrp$train.plot
subgrp$test.plot
#subgrp$cv.res$stats.summary #CV estimates of all results

summarize.cv.stats

Description

Calculate summary statistics from raw statistics returned by evaluate.cv.results.

Usage

summarize.cv.stats(raw.stats, trtvar, type)

Arguments

raw.stats

raw statistics from evaluate.cv.results
trtvar

treatment variable name
type

data type - "c" - continuous , "b" - binary, "s" - time to event - default = "c"

Details

Calculate summary statistics from raw statistics returned by evaluate.cv.results.

Value

a list containing p-values, summary statistics and group statistics.