BACKGROUND: 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. METHODS: We describe the Bayesian meta-regression implementation provided in the bayesmeta R package 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, subgroup 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 simulation studies.
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