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arXiv · 2202.12538

Summarizing empirical information on between-study heterogeneity for Bayesian random-effects meta-analysis

Christian Röver·Sibylle Sturtz·Jona Lilienthal·Ralf Bender·Tim Friede
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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.

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Christian Röver, Sibylle Sturtz, Jona Lilienthal, Ralf Bender, Tim Friede. 2022-02-25. Summarizing empirical information on between-study heterogeneity for Bayesian random-effects meta-analysis. https://doi.org/10.1002/sim.9731

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