Applying Meta-Analytic-Predictive Priors with the R Bayesian Evidence Synthesis Tools

Applying Meta-Analytic-Predictive Priors with the R Bayesian Evidence Synthesis Tools

Bayesian inference, clinical trial, extrapolation, historical control, operating characteristics, prior, probability of success, robust analysis.

Use of historical data in clinical trial design and analysis has shown various advantages such as reduction of number of subjects and increase of study power. The metaanalytic-predictive (MAP) approach accounts with a hierarchical model for between-trial
heterogeneity in order to derive an informative prior from historical 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 Markov chain Monte
Carlo (MCMC). The 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 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.

Sebastian Weber

https://www.jstatsoft.org/article/view/v100i19

Novartis Pharma AG

November 2021

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