# Bayesian workflow for bias-adjustment model in meta-analysis

## TL;DR

This paper introduces a Bayesian workflow to improve the robustness of bias-adjustment models in meta-analysis, showing how prior choices affect results and model reliability.

## Contribution

A systematic Bayesian workflow is proposed to evaluate and apply bias-adjustment models in meta-analysis with greater transparency and robustness.

## Key findings

- Results are highly sensitive to the prior on bias probability in the Bayesian workflow.
- The random-effects model showed better predictive accuracy than the bias-adjustment model.
- The bias-adjustment model produced wider credible intervals, reflecting greater uncertainty.

## 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.