bacisClassification {bacistool}

Conduct classification for subgroups.

Description

The classification model is conducted based on the BaCIS method and the subgroupos are classified into two clusters: high respone rate cluster and low response rate cluster.

Usage

bacisClassification(numGroup, tau1, tau2, phi1, phi2,
                    clusterCutoff, MCNum, nDat, xDat, seed)

Arguments

numGroup

Number of subgroups in the trial.

tau1

The precision parameter of subgroups clustering for the classification model.

tau2

The precision prior for the latent variable for the classification.

phi1

Center for the low response rate cluster.

phi2

Center for the high response rate cluster.

clusterCutoff

The cutoff value of the cluster classification. If its value is NA, adaptive classification is applied.

MCNum

The number of MCMC sampling iterations.

nDat

The vector of total sample sizes of all subgroups.

xDat

The vector of the response numbers of all subgroups.

seed

Random seed value. If its value is NA, a time dependent random seed is generated and applied.

Value

The classification model is applied using the input parameter values and subgroup outcomes. The classification results are returned. The return list includes highResponseGroup and lowResponseGroup index vlaues.

Author(s)

Nan Chen and J. Jack Lee / Department of Biostatistics UT MD Anderson Cancer Center

Examples

## An example to conduct subgroup classification.

library(bacistool)
result<-bacisClassification(numGroup=5,
                      tau1=NA,
                      tau2=.001,
                      phi1=0.1, phi2=0.3,
                      clusterCutoff = NA,
                      MCNum=5000,
                      nDat=c(25,25,25,25,25),
                      xDat=c(3,4,3,8,7),seed=100)