Summarizing empirical information on between-study heterogeneity for Bayesian random-effects meta-analysis
arXiv:2202.12538 · doi:10.1002/sim.9731
Abstract
In Bayesian meta-analysis, the specification of prior probabilities for the between-study heterogeneity is commonly required, and is of particular benefit in situations where only few studies are included. Among the considerations in the set-up of such prior distributions, the consultation of available empirical data on a set of relevant past analyses sometimes plays a role. How exactly to summarize historical data sensibly is not immediately obvious; in particular, the investigation of an empirical collection of heterogeneity estimates will not target the actual problem and will usually only be of limited use. The commonly used normal-normal hierarchical model for random-effects meta-analysis is extended to infer a heterogeneity prior. Using an example data set, we demonstrate how to fit a distribution to empirically observed heterogeneity data from a set of meta-analyses. Considerations also include the choice of a parametric distribution family. Here, we focus on simple and readily applicable approaches to then translate these into (prior) probability distributions.
arXiv comment18 pages, 5 tables, 4 figures
References in corpus (9)
- Hartung-Knapp-Sidik-Jonkman approach and its modification for random-effects meta-analysis with few studies
- Bayesian random-effects meta-analysis using the bayesmeta R package
- Meta-analysis of few small studies in orphan diseases
- On weakly informative prior distributions for the heterogeneity parameter in Bayesian random-effects meta-analysis
- Likelihood-based meta-analysis with few studies: Empirical and simulation studies
- Meta-analysis of two studies in the presence of heterogeneity with applications in rare diseases
- Meta-analysis of few studies involving rare events
- Bayes, Jeffreys, Prior Distributions and the Philosophy of Statistics
- Model averaging for robust extrapolation in evidence synthesis