bhmbasket: Bayesian Hierarchical Models for Basket Trials

Provides functions for the evaluation of basket trial designs with binary endpoints. Operating characteristics of a basket trial design are assessed by simulating trial data according to scenarios, analyzing the data with Bayesian hierarchical models (BHMs), and assessing decision probabilities on stratum and trial-level based on Go / No-go decision making. The package is build for high flexibility regarding decision rules, number of interim analyses, number of strata, and recruitment. The BHMs proposed by Berry et al. (2013) <doi:10.1177/1740774513497539> and Neuenschwander et al. (2016) <doi:10.1002/pst.1730>, as well as a model that combines both approaches are implemented. Functions are provided to implement Bayesian decision rules as for example proposed by Fisch et al. (2015) <doi:10.1177/2168479014533970>. In addition, posterior point estimates (mean/median) and credible intervals for response rates and some model parameters can be calculated. For simulated trial data, bias and mean squared errors of posterior point estimates for response rates can be provided.

Version: 0.9.5
Depends: R (≥ 3.5.0)
Imports: foreach, doRNG, R2jags (≥ 0.7-1)
Suggests: doFuture, future, knitr, rmarkdown
Published: 2022-02-14
DOI: 10.32614/CRAN.package.bhmbasket
Author: Stephan Wojciekowski [aut, cre]
Maintainer: Stephan Wojciekowski <stephan.wojciekowski at>
License: GPL-3
NeedsCompilation: no
SystemRequirements: JAGS 4.x.y (
Citation: bhmbasket citation info
Materials: NEWS
CRAN checks: bhmbasket results


Reference manual: bhmbasket.pdf
Vignettes: Running bhmbasket on HPC
Reproducing Parts of Neuenschwander et al. (2016)


Package source: bhmbasket_0.9.5.tar.gz
Windows binaries: r-devel:, r-release:, r-oldrel:
macOS binaries: r-release (arm64): bhmbasket_0.9.5.tgz, r-oldrel (arm64): bhmbasket_0.9.5.tgz, r-release (x86_64): bhmbasket_0.9.5.tgz, r-oldrel (x86_64): bhmbasket_0.9.5.tgz
Old sources: bhmbasket archive

Reverse dependencies:

Reverse imports: basksim
Reverse suggests: oncomsm


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