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Bayesian workflow for bias-adjustment model in meta-analysis
Journal article   Open access   Peer reviewed

Bayesian workflow for bias-adjustment model in meta-analysis

Juyoung Jung and Ariel M. Aloe
Research synthesis methods, Vol.17(2), pp.293-313
03/2026
DOI: 10.1017/rsm.2025.10050
Appears in  UI Libraries Support Open Access
url
https://doi.org/10.1017/rsm.2025.10050View
Published (Version of record) Open Access

Abstract

Bayesian hierarchical models offer a principled framework for adjusting for study-level bias in meta-analysis, but their complexity and sensitivity to prior specifications necessitate a systematic framework for robust application. This study demonstrates the application of a Bayesian workflow to this challenge, comparing a standard random-effects model to a bias-adjustment model across a real-world dataset and a targeted simulation study. The workflow revealed a high sensitivity of results to the prior on bias probability, showing that while the simpler random-effects model had superior predictive accuracy as measured by the widely applicable information criterion, the bias-adjustment model successfully propagated uncertainty by producing wider, more conservative credible intervals. The simulation confirmed the model’s ability to recover true parameters when priors were well-specified. These results establish the Bayesian workflow as a principled framework for diagnosing model sensitivities and ensuring the transparent application of complex bias-adjustment models in evidence synthesis.
 Bayesian meta-analysis Bayesian workflow bias adjustment model validation risk of bias UIOWA OA Agreement

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