Consistent Bayesian meta-analysis on subgroup specific effects and interactions

TL;DR

This paper introduces a Bayesian meta-analysis method that adjusts for varying subgroup contributions, enabling consistent and reliable inference of subgroup effects and interactions across clinical trials.

Contribution

The paper presents a novel Bayesian framework for subgroup-data interaction meta-analysis that accounts for differing subgroup contributions and can incorporate prior information.

Findings

The method improves consistency of interaction inference across trials.

It is robust to prevalence imbalance and variation.

Application to multiple sclerosis trials demonstrates its effectiveness.

Abstract

Commonly, clinical trials report effects not only for the full study population but also for patient subgroups. Meta-analyses of subgroup-specific effects and treatment-by-subgroup interactions may be inconsistent, especially when trials apply different subgroup weightings. We show that meta-regression can, in principle, with a contribution adjustment, recover the same interaction inference regardless of whether interaction data or subgroup data are used. Our Bayesian framework for subgroup-data interaction meta-analysis inherently (i) adjusts for varying relative subgroup contribution, quantified by the information fraction (IF) within a trial; (ii) is robust to prevalence imbalance and variation; (iii) provides a self-contained, model-based approach; and (iv) can be used to incorporate prior information into interaction meta-analyses with few studies.The method is demonstrated using an example with as few as seven trials of disease-modifying therapies in relapsing-remitting multiple sclerosis. The Bayesian Contribution-adjusted Meta-analysis by Subgroup (CAMS) indicates a stronger treatment-by-disability interaction (relapse rate reduction) in patients with lower disability (EDSS <= 3.5) compared with the unadjusted model, while results for younger patients (age < 40 years) are unchanged.By controlling subgroup contribution while retaining subgroup interpretability, this approach enables reliable interaction decision-making when published subgroup data are available.Although the proposed CAMS approach is presented in a Bayesian context, it can also be implemented in frequentist or likelihood frameworks.