Bodnar Olha, Link Alfred, Arendacká Barbora, Possolo Antonio, Elster Clemens
Physikalisch-Technische Bundesanstalt, Abbestrasse 2-12, Berlin, 10587, Germany.
Institut für Medizinische Statistik, Humboldtallee 32, Göttingen, Germany.
Stat Med. 2017 Jan 30;36(2):378-399. doi: 10.1002/sim.7156. Epub 2016 Oct 28.
Pooling information from multiple, independent studies (meta-analysis) adds great value to medical research. Random effects models are widely used for this purpose. However, there are many different ways of estimating model parameters, and the choice of estimation procedure may be influential upon the conclusions of the meta-analysis. In this paper, we describe a recently proposed Bayesian estimation procedure and compare it with a profile likelihood method and with the DerSimonian-Laird and Mandel-Paule estimators including the Knapp-Hartung correction. The Bayesian procedure uses a non-informative prior for the overall mean and the between-study standard deviation that is determined by the Berger and Bernardo reference prior principle. The comparison of these procedures focuses on the frequentist properties of interval estimates for the overall mean. The results of our simulation study reveal that the Bayesian approach is a promising alternative producing more accurate interval estimates than those three conventional procedures for meta-analysis. The Bayesian procedure is also illustrated using three examples of meta-analysis involving real data. Copyright © 2016 John Wiley & Sons, Ltd.
整合来自多个独立研究的信息（荟萃分析）为医学研究增添了巨大价值。随机效应模型广泛用于此目的。然而，估计模型参数有许多不同方法，估计程序的选择可能会对荟萃分析的结论产生影响。在本文中，我们描述了一种最近提出的贝叶斯估计程序，并将其与轮廓似然法以及包括克纳普 - 哈通校正的德西蒙尼安 - 莱尔德估计器和曼德尔 - 保勒估计器进行比较。贝叶斯程序对总体均值和研究间标准差使用由伯杰和贝尔纳多参考先验原理确定的非信息性先验。这些程序的比较集中在总体均值区间估计的频率特性上。我们模拟研究的结果表明，贝叶斯方法是一种很有前景的替代方法，它产生的区间估计比荟萃分析的那三种传统程序更准确。还使用涉及实际数据的三个荟萃分析示例说明了贝叶斯程序。版权所有© 2016约翰威立父子有限公司。
