Bayes Factor Hypothesis Testing in Meta-Analyses: Practical Advantages and Methodological Considerations

TL;DR

This paper discusses the advantages of Bayesian Bayes factor hypothesis testing in meta-analyses, highlighting its theoretical, methodological, and practical benefits over traditional p-value methods.

Contribution

It provides a comprehensive overview of Bayes factors in meta-analysis, including recent theoretical advances, methodological considerations, and practical applications with new R tools.

Findings

Bayes factors enable support quantification for and against effects.

They facilitate ongoing evidence monitoring in meta-analyses.

New tools in R package BFpack support these methods.

Abstract

Bayesian hypothesis testing via Bayes factors offers a principled alternative to classical p-value methods in meta-analysis, particularly suited to its cumulative and sequential nature. Unlike commonly reported p-values for standard null hypothesis significance testing, Bayes factors allow for quantifying support both for and against the existence of an effect, facilitate ongoing evidence monitoring, and maintain coherent long-run behavior as additional studies are incorporated. Recent theoretical developments further show how Bayes factors can flexibly control Type I error rates through connections to e-value theory. Despite these advantages, their use remains limited in the meta-analytic literature. This paper provides a critical overview of their theoretical properties, methodological considerations, such as prior sensitivity, and practical advantages for evidence synthesis. Two illustrative applications are provided: one on statistical learning in individuals with language impairments, and another on seroma incidence following post-operative exercise in breast cancer patients. New tools supporting these methods are available in the open-source R package BFpack.