Liu Yulun, DeSantis Stacia M, Chen Yong
Department of Biostatistics, The University of Texas Health Science Center Houston, Houston, Texas 77030, U.S.A.
Department of Biostatistics and Epidemiology, University of Pennsylvania, Philadelphia, Pennsylvania, 19104, U.S.A.
J R Stat Soc Ser C Appl Stat. 2018 Jan;67(1):127-144. doi: 10.1111/rssc.12220. Epub 2017 Mar 17.
Many randomized controlled trials (RCTs) report more than one primary outcome. As a result, multivariate meta-analytic methods for the assimilation of treatment effects in systematic reviews of RCTs have received increasing attention in the literature. These methods show promise with respect to bias reduction and efficiency gain compared to univariate meta-analysis. However, most methods for multivariate meta-analysis have focused on pairwise treatment comparisons (i.e., when the number of treatments is two). Current methods for mixed treatment comparisons (MTC) meta-analysis (i.e., when the number of treatments is more than two) have focused on univariate or very recently, bivariate outcomes. To broaden their application, we propose a framework for MTC meta-analysis of multivariate (≥ 2) outcomes where the correlations among multivariate outcomes within- and between-studies are accounted for through copulas, and the joint modeling of multivariate random effects, respectively. We consider a Bayesian hierarchical model using Markov Chain Monte Carlo methods for estimation. An important feature of the proposed framework is that it allows for borrowing of information across correlated outcomes. We show via simulation that our approach reduces the impact of outcome reporting bias (ORB) in a variety of missing outcome scenarios. We apply the method to a systematic review of RCTs of pharmacological treatments for alcohol dependence, which tends to report multiple outcomes potentially subject to ORB.
许多随机对照试验（RCT）报告了不止一个主要结局。因此，在RCT的系统评价中用于合并治疗效果的多变量荟萃分析方法在文献中受到了越来越多的关注。与单变量荟萃分析相比，这些方法在减少偏差和提高效率方面显示出前景。然而，大多数多变量荟萃分析方法都集中在成对治疗比较上（即治疗数为两个时）。当前用于混合治疗比较（MTC）荟萃分析的方法（即治疗数超过两个时）集中在单变量或最近的双变量结局上。为了拓宽其应用范围，我们提出了一个用于多变量（≥2）结局的MTC荟萃分析框架，其中通过copulas分别考虑研究内和研究间多变量结局之间的相关性以及多变量随机效应的联合建模。我们考虑使用马尔可夫链蒙特卡罗方法进行估计的贝叶斯分层模型。所提出框架的一个重要特征是它允许跨相关结局借用信息。我们通过模拟表明，我们的方法在各种缺失结局情况下减少了结局报告偏差（ORB）的影响。我们将该方法应用于酒精依赖药物治疗RCT的系统评价，该评价倾向于报告多个可能受到ORB影响的结局。
