Package: bacistool 1.0.0

J. Jack Lee

bacistool: Bayesian Classification and Information Sharing (BaCIS) Tool for the Design of Multi-Group Phase II Clinical Trials

Provides the design of multi-group phase II clinical trials with binary outcomes using the hierarchical Bayesian classification and information sharing (BaCIS) model. Subgroups are classified into two clusters on the basis of their outcomes mimicking the hypothesis testing framework. Subsequently, information sharing takes place within subgroups in the same cluster, rather than across all subgroups. This method can be applied to the design and analysis of multi-group clinical trials with binary outcomes. Reference: Nan Chen and J. Jack Lee (2019) <doi:10.1002/bimj.201700275>.

Authors:Nan Chen and J. Jack Lee

bacistool_1.0.0.tar.gz
bacistool_1.0.0.tar.gz(r-4.5-noble)bacistool_1.0.0.tar.gz(r-4.4-noble)
bacistool_1.0.0.tgz(r-4.4-emscripten)bacistool_1.0.0.tgz(r-4.3-emscripten)
bacistool.pdf |bacistool.html
bacistool/json (API)

# Install 'bacistool' in R:
install.packages('bacistool', repos = c('https://cran.r-universe.dev', 'https://cloud.r-project.org'))

Peer review:

Uses libs:
  • jags– Just Another Gibbs Sampler for Bayesian MCMC
  • c++– GNU Standard C++ Library v3

This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.

jagscpp

1.11 score 13 scripts 273 downloads 6 exports 3 dependencies

Last updated 5 years agofrom:a7a336e0ee. Checks:OK: 2. Indexed: no.

TargetResultDate
Doc / VignettesOKDec 01 2024
R-4.5-linuxOKDec 01 2024

Exports:bacisCheckDICbacisClassificationbacisOneTrialbacisPlotClassificationbacisSubgroupPosteriorbacisThetaPosterior

Dependencies:codalatticerjags