Bayesian Model-Averaged Meta-Analysis in Medicine

TL;DR

This paper develops a Bayesian model-averaged approach for meta-analysis in medicine, assessing treatment effects and heterogeneity, and proposes empirical priors tailored for different medical fields.

Contribution

It introduces a Bayesian model-averaged meta-analysis framework with empirically derived priors, improving evidence quantification across diverse medical disciplines.

Findings

Model assuming treatment effect and heterogeneity outperforms others

Empirical priors are effective across medical subdisciplines

Proposed method is implemented in R and JASP for practical use

Abstract

We outline a Bayesian model-averaged meta-analysis for standardized mean differences in order to quantify evidence for both treatment effectiveness $\delta$ and across-study heterogeneity $\tau$. We construct four competing models by orthogonally combining two present-absent assumptions, one for the treatment effect and one for across-study heterogeneity. To inform the choice of prior distributions for the model parameters, we used 50% of the Cochrane Database of Systematic Reviews to specify rival prior distributions for $\delta$ and $\tau$. The relative predictive performance of the competing models and rival prior distributions was assessed using the remaining 50\% of the Cochrane Database. On average, $\mathcal{H}_1^r$ -- the model that assumes the presence of a treatment effect as well as across-study heterogeneity -- outpredicted the other models, but not by a large margin. Within $\mathcal{H}_1^r$, predictive adequacy was relatively constant across the rival prior distributions. We propose specific empirical prior distributions, both for the field in general and for each of 46 specific medical subdisciplines. An example from oral health demonstrates how the proposed prior distributions can be used to conduct a Bayesian model-averaged meta-analysis in the open-source software R and JASP. The preregistered analysis plan is available at https://osf.io/zs3df/.