Röver Christian, Bender Ralf, Dias Sofia, Schmid Christopher H, Schmidli Heinz, Sturtz Sibylle, Weber Sebastian, Friede Tim
Department of Medical Statistics, University Medical Center Göttingen, Göttingen, Germany.
Department of Medical Biometry, Institute for Quality and Efficiency in Health Care (IQWiG), Köln, Germany.
Res Synth Methods. 2021 Jul;12(4):448-474. doi: 10.1002/jrsm.1475. Epub 2021 Feb 15.
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 noninformative 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.
正态-正态层次模型（NNHM）构成了一种简单且广泛应用于荟萃分析的框架。在仅有少数研究对荟萃分析有贡献的常见情况下，标准的推断方法往往表现不佳，因此有人建议采用贝叶斯荟萃分析作为一种潜在的解决方案。然而，贝叶斯方法需要合理指定先验分布。虽然非信息性先验通常用于总体平均效应，但有人建议对异质性参数使用弱信息性先验，特别是在（非常）少的研究情况下。然而，迄今为止，对于如何一般地指定弱信息性异质性先验尚无共识。在这里，我们更深入地研究这个问题，并提供一些关于先验指定的指导。
