Bibliographic Details
| Title: |
Summarizing empirical information on between-study heterogeneity for Bayesian random-effects meta-analysis |
| Authors: |
Röver, Christian, Sturtz, Sibylle, Lilienthal, Jona, Bender, Ralf, Friede, Tim |
| Source: |
Statistics in Medicine, 42(14):2439-2454, 2023 |
| Publication Year: |
2022 |
| Collection: |
Statistics |
| Subject Terms: |
Statistics - Methodology |
| More Details: |
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.
Comment: 18 pages, 5 tables, 4 figures |
| Document Type: |
Working Paper |
| DOI: |
10.1002/sim.9731 |
| Access URL: |
http://arxiv.org/abs/2202.12538 |
| Accession Number: |
edsarx.2202.12538 |
| Database: |
arXiv |
