SubgrpID: Exploratory Subgroup Identification main function

Description Usage Arguments Details Value Author(s) References Examples

View source: R/master_function.R

Description

Prognostic and predictive biomarker signature development for Exploratory Subgroup Identification in Randomized Clinical Trials

Usage

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SubgrpID(data.train, data.test=NULL, 
         yvar, 
         censorvar=NULL, 
         trtvar=NULL, 
         trtref=NULL, 
         xvars, 
         type="c",
         n.boot=ifelse(method=="PRIM",0,25),
         des.res="larger",
         min.sigp.prcnt=0.20, 
         pre.filter=NULL, 
         filter.method=NULL, 
         k.fold=5, 
         cv.iter=20, 
         max.iter=500, 
         mc.iter=20, 
         method=c("AIM.Rule"), 
         train.percent.prim=0.5, 
         do.cv=FALSE, 
         out.file=NULL, 
         file.path="", 
         plots=F)

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

algorithms performed for subgroup identification, one of the ("AIM", "AIM.Rule", "Seq.BT", "PRIM")

train.percent.prim

percentage of the sub-training set used only by PRIM method; if train.percent.prim=1, all data will be used both for sub-training and sub-testing inside the PRIM

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: F. whether to save plots

Details

The function contains four algorithms for developing threshold-based multivariate (prognostic/predictive) biomarker signatures via resampled tree-based algorithms (Sequential BATTing), Monte-Carlo variations of the Adaptive Indexing method (AIM and AIM-Rule)and Patient Rule Induction Method. Variable selection is automatically built-in to these algorithms. Final signatures are returned with interaction plots for predictive signatures. Cross-validation performance evaluation and testing dataset results are also output.

Value

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

Author(s)

Xin Huang, Yan Sun, Saptarshi Chatterjee and Paul Trow

References

Huang X. et al. (2017) Patient subgroup identification for clinical drug development. Statistics in Medicine, doi: 10.1002/sim.7236.

Chen G. et al. (2015) A PRIM approach to predictive-signature development for patient stratification Statistics in Medicine, 34, 317-342.

Examples

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  ## Not run: 
    data(Sepsis.train)
    data(Sepsis.test)
    
    yvar="survival"
    xvars=names(Sepsis.train)[2:12]
    trtvar="THERAPY"
    
    set.seed(123)
    subgrp <- SubgrpID(data.train=Sepsis.train,
                      data.test=Sepsis.test,
                      yvar=yvar,
                      trtvar=trtvar,
                      trtref="active",
                      xvars=xvars,
                      type="b",
                      des.res = "smaller",
                      method="AIM.Rule")
    subgrp$res
    subgrp$train.stat
    subgrp$test.stat
    subgrp$train.plot
    subgrp$test.plot
  
## End(Not run)

SubgrpID documentation built on May 29, 2017, 9:38 p.m.