Using the bayesmeta R package for Bayesian random-effects meta-regression

Random-effects meta-analysis within a hierarchical normal modeling framework is commonly implemented in a wide range of evidence synthesis applications. More general problems may even be tackled when considering meta-regression approaches that in addition allow for the inclusion of study-level covariables. Here we describe the Bayesian meta-regression implementation provided in the bayesmeta R package including the choice of priors. To illustrate its practical use, a wide range of example applications are given such as binary and continuous covariables, subgroup analysis, indirect comparisons, and model selection. Example R code is provided, and, due to the avoidance of MCMC methods, computations are fast and reproducible, faciliting e.g. large-scale simulation studies.