Using the bayesmeta R package for Bayesian random-effects meta-regression
2023-02-01 | journal article. A publication with affiliation to the University of Göttingen.
Jump to: Cite & Linked | Documents & Media | Details | Version history
Documents & Media
Details
- Authors
- Röver, Christian ; Friede, Tim
- 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.
- Issue Date
- 1-February-2023
- Journal
- Computer Methods and Programs in Biomedicine
- Organization
- Institut für Medizinische Statistik
- ISSN
- 0169-2607
- eISSN
- 1872-7565
- Language
- English