Applying Meta-Analytic-Predictive Priors with the R Bayesian evidence synthesis tools

2019/07/01 by Sebastian Weber, Yue Li, Weber, Sebastian +9 · 8 citations
Decision Sciences · Mathematics · #Algorithm #Applications (stat.AP) #Artificial intelligence #Bayesian probability #Computer science #Data mining #FOS: Computer and information sciences #G.3.14 #J.3.1 #Machine learning #Markov chain Monte Carlo #Mathematical optimization #Mathematics #Maximization #Meta-analysis and systematic reviews #Parametric statistics #Prior probability #Statistical Methods and Bayesian Inference #Statistical Methods in Clinical Trials #Statistics #acm:G.3.14 #acm:J.3.1 #msc:G.3.14 #msc:J.3.1 #stat.AP

paper · pdf · doi:10.48550/arxiv.1907.00603

published in arXiv (Cornell University) (Cornell University) · 27 pages, 3 figures, RBesT R package on CRAN https://cran.r-project.org/package=RBesT

openalex publication_date 2019/07/01 · arxiv created 2019/12/11 · arxiv updated 2019/12/12 · openalex created_date 2022/07/28 · openalex updated_date 2026/07/28

Abstract

Use of historical data in clinical trial design and analysis has shown various advantages such as reduction of within-study placebo-treated number of subjects and increase of study power. The meta-analytic-predictive (MAP) approach accounts with a hierarchical model for between-trial heterogeneity in order to derive an informative prior from historical (often control) data. In this paper, we introduce the package RBesT (R Bayesian Evidence Synthesis Tools) which implements the MAP approach with normal (known sampling standard deviation), binomial and Poisson endpoints. The hierarchical MAP model is evaluated by MCMC. The numerical MCMC samples representing the MAP prior are approximated with parametric mixture densities which are obtained with the expectation maximization algorithm. The parametric mixture density representation facilitates easy communication of the MAP prior and enables via fast and accurate analytical procedures to evaluate properties of trial designs with informative MAP priors. The paper first introduces the framework of robust Bayesian evidence synthesis in this setting and then explains how RBesT facilitates the derivation and evaluation of an informative MAP prior from historical control data. In addition we describe how the meta-analytic framework relates to further applications including probability of success calculations.

Citations

• Practice policies: where do they come from?
• Robust mixture modelling using the t distribution
• Visualization in Bayesian workflow

Cited by

• Consistent Bayesian meta-analysis on subgroup specific effects and interactions
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• Principled type I error rate inflation in two-arm clinical trial designs with external control information borrowing
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• Statistical Methodology Groups in the Pharmaceutical Industry
• CCMnet: A Software Package for Network Generation with Congruence Class Models
• Bayesian random-effects meta-analysis of aggregate data on clinical events

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