Title: Incorporating genuine prior information about between-study heterogeneity in random effects pairwise and network meta-analyses,
Abstract: Background: Pairwise and network meta-analyses using fixed effect and random
effects models are commonly applied to synthesise evidence from randomised
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
standard deviation from the data alone. Objectives: The aim is to propose a
framework for eliciting an informative prior distribution for the between-study
standard deviation 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
experts' beliefs on the 'range' of treatment effects in order to infer the
prior distribution for the between-study standard deviation. We also developed
the method to be implemented in R. Results: The three-stage elicitation
approach allows uncertainty to be represented by a genuine prior distribution
to avoid making misleading inferences. It is flexible to what judgments an
expert can provide, and is applicable to all types of outcome measure for which
a treatment effect can be constructed on an additive scale. Conclusions: The
choice between using a fixed effect or random effects meta-analysis model
depends on the inferences required and not on the number of available studies.
Our elicitation framework captures external evidence about heterogeneity and
overcomes the often implausible assumption that studies are estimating the same
treatment effect, thereby improving the quality of inferences in decision
making.