An informed reference prior for between-study heterogeneity in meta-analyses of binary outcomes.
It is well known that when a Bayesian meta-analysis includes a small number of studies, inference can be sensitive to the choice of prior for the between-study variance. Choosing a vague prior does not solve the problem, as inferences can be substantially different depending on the degree of vagueness. Moreover, because the data provide little information on between-study heterogeneity, posterior inferences for the between-study variance based on vague priors will tend to be unrealistic. It is thus preferable to adopt a reasonable, informed prior for the between-study variance. However, relatively little is known about what constitutes a realistic distribution. On the basis of data from the Cochrane Database of Systematic Reviews, this paper describes the distribution of between-study variance in published meta-analyses, and proposes some realistic, informed priors for use in meta-analyses of binary outcomes. It is hoped that these priors will improve the calibration of inferences from Bayesian meta-analyses.
Department of Clinical Epidemiologyand Biostatistics, McMaster University, Hamilton, ON, Canada. pullena@mcmaster.ca
Eleanor M
EM
Pullenayegum
D001499:Bayes Theorem; D003198:Computer Simulation; D006801:Humans; D015201:Meta-Analysis as Topic; D010033:Otitis Media / Q000453:epidemiology