Using the bayesmeta R package for Bayesian random-effects meta-regression. (February 2023)

• Record Type: Journal Article
• Authors: Röver, Christian; Friede, Tim
• Is Part Of: Computer methods and programs in biomedicine. Volume 229(2023)
• Publication Date: 2023-02
• Subjects: Meta-analysis -- Subgroup analysis -- Covariables -- Moderators -- Heterogeneity; Medicine -- Computer programs -- Periodicals; Biology -- Computer programs -- Periodicals; Computers -- Periodicals; Medicine -- Periodicals; Médecine -- Logiciels -- Périodiques; Biologie -- Logiciels -- Périodiques; Biology -- Computer programs; Medicine -- Computer programs; Periodicals; Electronic journals; 610.28
• DOI: 10.1016/j.cmpb.2022.107303
• Languages: English
• ISSNs: 0169-2607

Abstract

Abstract: 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.