SubgrpID: Exploratory Subgroup Identification main function
In SubgrpID: Patient Subgroup Identification for Clinical Drug Development

Description Usage Arguments Details Value Author(s) References Examples

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

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)

`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

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.

`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

Xin Huang, Yan Sun, Saptarshi Chatterjee and Paul Trow

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.

  ## 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)