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arXiv · 2511.22535

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

Joris Mulder·Robbie C. M. van Aert
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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.

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Joris Mulder, Robbie C. M. van Aert. 2025-11-27. Bayes Factor Hypothesis Testing in Meta-Analyses: Practical Advantages and Methodological Considerations. https://doi.org/10.1017/rsm.2025.10060

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