SubgrpID: SubgrpID

In SubgrpID: Patient Subgroup Identification for Clinical Drug Development

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

SubgrpID documentation built on May 29, 2024, 1:33 a.m.