Bayesian meta-analysis is easier than you think

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 Xexible 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 Rndings from our comprehensive review of models and software in preparation for a new book, Bayesian Meta-Analysis: a practical introduction, from a scoping review, and from 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 nonstatistical researchers, illustrating simple and Xexible 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 speciRcation and model validation to complete a reliable Bayesian workXow. Importantly, regulatory agencies and major journals increasingly recognize the value of Bayesian meta-analytic approaches, reXecting their growing adoption in high-impact research synthesis.