bacistool (version 1.0.0)

bacisSubgroupPosterior: Compute the posterior distribution of response rates of subgroups using the BaCIS method.

Description

In this function, a trial computation is conducted based on the BaCIS model. It calls the JAGS for the Bayesian MCMC sampling for the subgroup classification and hierarchical model information borrowing. The response rate posterior distributions of subgroups are returned from this function.

Usage

bacisSubgroupPosterior(numGroup, tau1, tau2, phi1, phi2, tau4, alpha, beta,
                      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.

tau4

The precision prior for the center of the cluster in the information borrowing model.

alpha

Hyperprior parameters alpha to control the magnitude of information borrowing model.

beta

Hyperprior parameters beta to control the magnitude of the information borrowing model.

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 MCMC sampling data of the response rate posterior distributions of all subgroups is returned as an matrix format. Each column of the return matrix corresponds to the response rate distribution of one subgroup.

Examples

# NOT RUN {
## Compute the response rate posterior distributioni
## of each subgroup using the BaCIS method
# }
# NOT RUN {
library(bacistool)
result<-bacisSubgroupPosterior(numGroup=5,
                               tau1=NA,
                               tau2=.001,
                               phi1=0.1, phi2=0.3,
                               tau4=0.1,
                               alpha=50,
                               beta=20,
                               clusterCutoff=NA,
                               MCNum=5000,
                               nDat=c(25,25,25,25,25),
                               xDat=c(3,4,3,8,7))


# }