Incorporating genuine prior information about between-study heterogeneity in random effects pairwise and network meta-analyses

Background. Pairwise and network meta-analyses using fixed effect and random effects models are commonly applied to synthesize evidence from randomized controlled trials. The models differ in their assumptions and the interpretation of the results. The model choice depends on the objective of the analysis and knowledge of the included studies. Fixed effect models are often used because there are too few studies with which to estimate the between-study SD from the data alone. Objectives. The aim of this study was to propose a framework for eliciting an informative prior distribution for the between-study SD in a Bayesian random effects meta-analysis model to genuinely represent heterogeneity when data are sparse. Methods. We developed an elicitation method using external information, such as empirical evidence and expert beliefs, on the “range” of treatment effects to infer the prior distribution for the between-study SD. We also developed the method to be implemented in R. Results. The 3-stage elicitation...