Installation
About
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 R package offers functions for simulating, analyzing, and evaluating Bayesian MCPMod trials with normally 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.
Citation | BayesianMCPMod citation info |
github.com/Boehringer-Ingelheim/BayesianMCPMod | |
Bug report | File report |
Key Metrics
Downloads
Yesterday | 7 0% |
Last 7 days | 44 -19% |
Last 30 days | 267 +76% |
Last 90 days | 541 +42% |
Last 365 days | 920 |