Demystifying Bayesian meta-analysis for researchers
Challenging the perception that Bayesian methods for meta-analysis are inaccessible to researchers with practical guidance on prior specification and model validation.
Bayesian models offer a powerful framework for meta-analysis through their flexible and probabilistic treatment of uncertainty.
There are several methodological challenges in evidence synthesis, particularly where conventional asymptotic approximations become unreliable. These can include: small studies and small sample sizes, evidence from beyond the study data, systematic biases, and missing study information. Bayesian meta-analysis extends naturally to network meta-analysis and living evidence synthesis from its foundations as a class of multilevel models. Bayesian probabilistic outputs also directly addresses the needs of decision-makers at the population level, and allow easier interpretation.
This seminar provides a sneak preview of a forthcoming book from Robert Grant and Gian Luca Di Tanna on Bayesian meta-analysis. In it, they challenge the perception that Bayesian methods are inaccessible to researchers, and provide practical guidance on prior specification and model validation. Importantly, major journals increasingly recognize the value of Bayesian meta-analytic approaches, reflecting their growing adoption in high-impact research synthesis, and warming attitudes at the FDA and EMA.
Speakers
Robert Grant, Statistician and Director of BayesCamp
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