Bayesian meta-analysis is easier than you think

Session

Meta-analysis presents several methodological challenges when synthesizing evidence across studies, particularly in scenarios where conventional asymptotic approximations become unreliable. Bayesian methods offer a natural framework for evidence synthesis through their flexible treatment of uncertainty. The Bayesian paradigm accommodates sparse data structures, evidence beyond the study data, systematic biases, and missing study information. It leads to probabilistic outputs that directly address decision-makers' needs and allow easier interpretation. We present findings from our comprehensive review of models and software in preparation for a new book, “Bayesian Meta-Analysis: a practical introduction”, and from a scoping review, and its ongoing update. This has shown the potential for many widespread problems in meta-analysis to be addressed in the near future. We challenge the perception that Bayesian methods are inaccessible to non-statistical researchers, illustrating simple and flexible implementation in Stata. Bayesian meta-analysis extends naturally to network meta-analysis and living evidence synthesis from its foundations as a class of multilevel models. We also present practical guidance on prior specification and model validation to complete a reliable Bayesian workflow. Importantly, regulatory agencies and major journals increasingly recognize the value of Bayesian meta-analytic approaches, reflecting their growing adoption in high-impact research synthesis.