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

Background: Random-effects meta-analysis within a hierarchical normal modeling framework is com-monly 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.Methods: We describe the Bayesian meta-regression implementation provided in the bayesmeta R pack-age including the choice of priors, and we illustrate its practical use.Results: A wide range of example applications are given, such as binary and continuous covariables, sub-group analysis, indirect comparisons, and model selection. Example R code is provided.Conclusions: The bayesmeta package provides a flexible implementation. Due to the avoidance of MCMC methods, computations are fast and reproducible, facilitating quick sensitivity checks or large-scale sim-ulation studies.(c) 2022 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY license ( http://creativecommons.org/licenses/by/4.0/ )