Robust Bayesian meta-regression: Model-averaged moderation analysis in the presence of publication bias.

Abstract

Meta-regression is an essential meta-analytic tool for investigating sources of heterogeneity and assessing the impact of moderators. However, existing methods for meta-regression have limitations, such as inadequate consideration of model uncertainty and poor performance under publication bias. To overcome these limitations, we extend robust Bayesian meta-analysis (RoBMA) to meta-regression (RoBMA-regression). RoBMA-regression allows for moderator analyses while simultaneously taking into account the uncertainty about the presence and impact of other factors (i.e., the main effect, heterogeneity, publication bias, and other potential moderators). The methodology presents a coherent way of assessing the evidence for and against the presence of both continuous and categorical moderators. We further employ a Savage-Dickey density ratio test to quantify the evidence for and against the presence of the effect at different levels of categorical moderators. We illustrate RoBMA-regression in an empirical example and demonstrate its performance in a simulation study. We implemented the methodology in the RoBMA R package. Overall, RoBMA-regression presents researchers with a powerful and flexible tool for conducting robust and informative meta-regression analyses. (PsycInfo Database Record (c) 2025 APA, all rights reserved).