Bayesian random-effects meta-analysis using the bayesmeta R package

TL;DR

This paper introduces the bayesmeta R package, which simplifies Bayesian random-effects meta-analysis by providing accessible tools for inference, visualization, and sensitivity analysis, especially useful with few studies.

Contribution

The paper presents the bayesmeta R package, offering a user-friendly implementation of Bayesian meta-analysis with flexible priors and instant posterior summaries.

Findings

Easy-to-use tools for Bayesian meta-analysis in R

Supports flexible prior choices and sensitivity analysis

Provides quick access to posterior distributions and predictions

Abstract

The random-effects or normal-normal hierarchical model is commonly utilized in a wide range of meta-analysis applications. A Bayesian approach to inference is very attractive in this context, especially when a meta-analysis is based only on few studies. The bayesmeta R package provides readily accessible tools to perform Bayesian meta-analyses and generate plots and summaries, without having to worry about computational details. It allows for flexible prior specification and instant access to the resulting posterior distributions, including prediction and shrinkage estimation, and facilitating for example quick sensitivity checks. The present paper introduces the underlying theory and showcases its usage.