期刊
STATISTICS IN MEDICINE
卷 42, 期 14, 页码 2439-2454出版社
WILEY
DOI: 10.1002/sim.9731
关键词
external information; heterogeneity; hierarchical model; meta-analysis; prior distribution
In Bayesian meta-analysis, the specification of prior probabilities for heterogeneity is important, especially when there are few included studies. Utilizing empirical data from past analyses to inform the prior distributions is not straightforward. This study extends the commonly used normal-normal hierarchical model to infer a heterogeneity prior and presents simple approaches to fitting distributions to observed heterogeneity data.
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.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据