论文标题
关于贝叶斯随机效应的异质性参数荟萃分析的弱信息信息
On weakly informative prior distributions for the heterogeneity parameter in Bayesian random-effects meta-analysis
论文作者
论文摘要
正常正常分层模型（NNHM）构成了一个简单且广泛使用的荟萃分析框架。在只有很少有助于荟萃分析的研究的常见情况下，标准的推理方法往往会表现不佳，并且已经提出了贝叶斯荟萃分析作为潜在的解决方案。但是，贝叶斯方法需要明智的先验分布规范。尽管非信息性先验通常用于整体平均效应，但已提出了弱信息性先验的使用，特别是在（非常）很少的研究的情况下，尤其是在（尤其是）。然而，迄今为止，关于如何在缺乏弱信息的异质性方面达成共识。在这里，我们更仔细地调查问题，并为先前规范提供一些指导。
The normal-normal hierarchical model (NNHM) constitutes a simple and widely used framework for meta-analysis. In the common case of only few studies contributing to the meta-analysis, standard approaches to inference tend to perform poorly, and Bayesian meta-analysis has been suggested as a potential solution. The Bayesian approach, however, requires the sensible specification of prior distributions. While non-informative priors are commonly used for the overall mean effect, the use of weakly informative priors has been suggested for the heterogeneity parameter, in particular in the setting of (very) few studies. To date, however, a consensus on how to generally specify a weakly informative heterogeneity prior is lacking. Here we investigate the problem more closely and provide some guidance on prior specification.
