Robust Bayesian meta-regression

Date of Seminar: November 17, 2023 Speaker: František Bartoš, University of Amsterdam Description: Meta-regression constitutes an essential meta-analytic tool for investigating sources of heterogeneity and assessing the impact of moderators. However, existing methods for meta-regression have limitations that include 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 uncertainties about the presence and impact of other factors (i.e., the main effect, heterogeneity, publication bias, and other potential moderators). We offer guidance on how to specify prior distributions for continuous and categorical moderators and introduce 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.