Using the bayesmeta R package for Bayesian random-effects meta-regression
arXiv:2209.06004 · doi:10.1016/j.cmpb.2022.107303
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.
arXiv comment17 pages, 8 figures
References in corpus (8)
- Optimal predictive model selection
- Bayesian random-effects meta-analysis using the bayesmeta R package
- Meta-analysis of few small studies in orphan diseases
- On weakly informative prior distributions for the heterogeneity parameter in Bayesian random-effects meta-analysis
- Meta-analysis of few studies involving rare events
- Interleukin-2 receptor antagonists for pediatric liver transplant recipients: A systematic review and meta-analysis of controlled studies
- Model averaging for robust extrapolation in evidence synthesis
- Discrete approximation of a mixture distribution via restricted divergence