An evaluation of bivariate random-effects meta-analysis for the joint synthesis of two correlated outcomes
An evaluation of bivariate random-effects meta-analysis for the joint synthesis of two correlated outcomes
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DOI：
10.1002/sim.2524
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发表时间：
2007-01-15
影响因子：
2
通讯作者：
Thompson, J. R.
中科院分区：
文献类型：
--
作者：
Riley, R. D.;Abrams, K. R.;Thompson, J. R.
通常，在系统性综述确定的每项研究中，多个结果都是感兴趣的，在这种情况下，通常应用单独的单变量荟萃分析来独立地综合每个结果的证据;另一种方法是单一的多变量荟萃分析模型，该模型利用结果之间的任何相关性，并联合获得所有汇总估计值。令人惊讶的是，多变量荟萃分析很少被认为是在实践中，所以在本文中，我们说明的好处和局限性的方法，提供有益的洞察从业者。我们比较了一个双变量随机效应荟萃分析（BRMA），两个独立的单变量随机效应荟萃分析（乌尔马），并显示如何以及为什么BRMA是能够“借势”的结果。然后，在应用于两个例子，在医疗保健，我们表明：（i）在每个研究中的两个结果的完整数据，BRMA可能会产生非常相似的标准误差，从乌尔马的个人汇总估计;（ii）鉴于一些研究的结果之一是随机缺失的，“借势”很可能使BRMA能够产生比乌尔马的标准误差明显更小的单个合并估计值;（iii）对于完整数据或缺失数据，BRMA将产生结果之间合并差异的更合适的标准误，因为它包含了它们的相关性，而使用乌尔马是不可能的;以及（iv）尽管有其优势，但由于难以获得拟合模型所需的研究内相关性，BRMA通常可能不可能。二元元回归和进一步的研究重点进行了讨论。版权所有（c）2006约翰威利父子有限公司。
Often multiple outcomes are of interest in each study identified by a systematic review, and in this situation a separate univariate meta-analysis is usually applied to synthesize the evidence for each outcome independently; an alternative approach is a single multivariate meta-analysis model that utilizes any correlation between outcomes and obtains all the pooled estimates jointly. Surprisingly, multivariate meta-analysis is rarely considered in practice, so in this paper we illustrate the benefits and limitations of the approach to provide helpful insight for practitioners.We compare a bivariate random-effects meta-analysis (BRMA) to two independent univariate random-effects meta-analyses (URMA), and show how and why a BRMA is able to 'borrow strength' across outcomes. Then, on application to two examples in healthcare, we show: (i) given complete data for both outcomes in each study, BRMA is likely to produce individual pooled estimates with very similar standard errors to those from URMA; (ii) given some studies where one of the outcomes is missing at random, the 'borrowing of strength' is likely to allow BRMA to produce individual pooled estimates with noticeably smaller standard errors than those from URMA; (iii) for either complete data or missing data, BRMA will produce a more appropriate standard error of the pooled difference between outcomes as it incorporates their correlation, which is not possible using URMA; and (iv) despite its advantages, BRMA may often not be possible due to the difficulty in obtaining the within-study correlations required to fit the model. Bivariate meta-regression and further research priorities are also discussed. Copyright (c) 2006 John Wiley & Sons, Ltd.