Summarizing empirical information on between‐study heterogeneity for Bayesian random‐effects meta‐analysis
2023-04-02 | journal article. A publication with affiliation to the University of Göttingen.
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Summarizing empirical information on between‐study heterogeneity for Bayesian random‐effects meta‐analysis
Röver, C.; Sturtz, S.; Lilienthal, J.; Bender, R. & Friede, T. (2023)
Statistics in Medicine, 42(14) art. sim.9731. DOI: https://doi.org/10.1002/sim.9731
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Details
- Authors
- Röver, Christian; Sturtz, Sibylle; Lilienthal, Jona; Bender, Ralf; Friede, Tim
- 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.
- Issue Date
- 2-April-2023
- Journal
- Statistics in Medicine
- Organization
- Institut für Medizinische Statistik
- ISSN
- 0277-6715; 1097-0258
- eISSN
- 1097-0258
- Language
- English
- Sponsor
- Deutsche Forschungsgemeinschaft http://dx.doi.org/10.13039/501100001659