Simulate, Evaluate, and Analyze Dose Finding Trials with
Bayesian MCPMod
Bayesian MCPMod (Fleischer et al. (2022)
) 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) ), 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) ).
Estimated dose-response relationships can be bootstrapped and
visualized.
The BayesianMCPmod package
Simulate, analyze, and evaluate Bayesian MCPMod trials with normally and binary distributed endpoints.
Installation
Install the package from CRAN using
install.packages("BayesianMCPMod")
The development version can be installed from this repository.
# install.packages("remotes")
remotes::install_github("https://github.com/Boehringer-Ingelheim/BayesianMCPmod")
Documentation
The package documentation is hosted here.