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

by   Christian Röver, et al.

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.



page 1


On weakly informative prior distributions for the heterogeneity parameter in Bayesian random-effects meta-analysis

The normal-normal hierarchical model (NNHM) constitutes a simple and wid...

Bayesian random-effects meta-analysis using the bayesmeta R package

The random-effects or normal-normal hierarchical model is commonly utili...

Likelihood-based meta-analysis with few studies: Empirical and simulation studies

Standard random-effects meta-analysis methods perform poorly when applie...

Bayesian Model-Averaged Meta-Analysis in Medicine

We outline a Bayesian model-averaged meta-analysis for standardized mean...

Dynamically borrowing strength from another study

Meta-analytic methods may be used to combine evidence from different sou...

Bayesian analysis of extreme values in economic indexes and climate data: Simulation and application

Mixed modeling of extreme values and random effects is relatively unexpl...
This week in AI

Get the week's most popular data science and artificial intelligence research sent straight to your inbox every Saturday.