  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.
