BayesianMCPMod: Simulate, Evaluate, and Analyze Dose Finding Trials with Bayesian MCPMod

Bayesian MCPMod (Fleischer et al. (2022) <doi:10.1002/pst.2193>) is an innovative method that improves the traditional MCPMod by systematically incorporating historical data, such as previous placebo group data. This package offers functions for simulating, analyzing, and evaluating Bayesian MCPMod trials with normally and binary distributed endpoints. It enables the assessment of trial designs incorporating historical data across various true dose-response relationships and sample sizes. Robust mixture prior distributions, such as those derived with the Meta-Analytic-Predictive approach (Schmidli et al. (2014) <doi:10.1111/biom.12242>), can be specified for each dose group. Resulting mixture posterior distributions are used in the Bayesian Multiple Comparison Procedure and modeling steps. The modeling step also includes a weighted model averaging approach (Pinheiro et al. (2014) <doi:10.1002/sim.6052>). Estimated dose-response relationships can be bootstrapped and visualized.

Version:	1.3.0
Depends:	R (≥ 4.2)
Imports:	checkmate, DoseFinding (≥ 1.1-1), dplyr, ggplot2, methods, nloptr, RBesT, stats, tidyr
Suggests:	clinDR, doFuture, future.apply, kableExtra, knitr, MCPModPack, reactable, rmarkdown, spelling, testthat (≥ 3.0.0), tibble
Published:	2026-02-23
DOI:	10.32614/CRAN.package.BayesianMCPMod
Author:	Boehringer Ingelheim Pharma GmbH & Co. KG [cph, fnd], Stephan Wojciekowski [aut, cre], Lars Andersen [aut], Jonas Schick [ctb], Sebastian Bossert [aut]
Maintainer:	Stephan Wojciekowski <stephan.wojciekowski at boehringer-ingelheim.com>
BugReports:	https://github.com/Boehringer-Ingelheim/BayesianMCPMod/issues
License:	Apache License (≥ 2)
URL:	https://boehringer-ingelheim.github.io/BayesianMCPMod/, https://github.com/Boehringer-Ingelheim/BayesianMCPMod
NeedsCompilation:	no
Language:	en-US
Citation:	BayesianMCPMod citation info
Materials:	README, NEWS
In views:	ClinicalTrials
CRAN checks:	BayesianMCPMod results

Documentation:

Reference manual:	BayesianMCPMod.html , BayesianMCPMod.pdf
Vignettes:	Comparison of Bayesian MCPMod and MCPMod (source, R code) Trial Simulation Example of Bayesian MCPMod for Continuous Data (source, R code) Trial Simulation Example of Bayesian MCPMod for Binary Data (source, R code) Trial Analysis Example of Bayesian MCPMod for Continuous Data (source, R code) Trial Analysis Example of Bayesian MCPMod for Binary Data (source, R code)

Downloads:

Package source:	BayesianMCPMod_1.3.0.tar.gz
Windows binaries:	r-devel: BayesianMCPMod_1.2.0.zip, r-release: BayesianMCPMod_1.2.0.zip, r-oldrel: BayesianMCPMod_1.2.0.zip
macOS binaries:	r-release (arm64): BayesianMCPMod_1.3.0.tgz, r-oldrel (arm64): BayesianMCPMod_1.3.0.tgz, r-release (x86_64): BayesianMCPMod_1.2.0.tgz, r-oldrel (x86_64): BayesianMCPMod_1.2.0.tgz
Old sources:	BayesianMCPMod archive

Linking:

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