Multivariate meta-analysis: a robust approach based on the theory of U-statistic
Multivariate meta-analysis: a robust approach based on the theory of U-statistic
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DOI：
10.1002/sim.4327
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发表时间：
2011-10-30
影响因子：
2
通讯作者：
Mazumdar, Madhu
中科院分区：
文献类型：
--
作者：
Ma, Yan;Mazumdar, Madhu
元分析是一种方法学，用于结合提出相同问题的类似研究的结果。当感兴趣的问题涉及多个结果时，多变量荟萃分析用于同时考虑结果之间的相关性来综合结果。基于似然的方法，特别是限制最大似然（REML）方法，通常用于这方面。REML假设随机效应模型为多元正态分布。这一假设很难验证，特别是对于包含少量成分研究的荟萃分析。REML的使用还需要参数之间的迭代估计，需要相当高的计算时间，特别是当结果的维度很大时。一个多变量的矩量法（MMM）是可用的，并表现出同样好的REML。然而，当真实数据分布远离正态分布时，缺乏关于这两种方法性能的信息。本文在U统计量理论的基础上，提出了一种新的非参数、非迭代的多元荟萃分析方法，并通过模拟研究比较了这三种方法在正态数据和偏态数据下的性能。结果表明，由于非正态数据分布的REML估计的影响是边际的，从MMM和U-统计为基础的方法的估计是非常相似的。因此，我们得出结论，进行多元荟萃分析，U-统计量估计程序是一个可行的替代REML和MMM。所有这三种方法的简单实施说明了他们的应用程序的数据，从两个发表的荟萃分析领域的髋部骨折和牙周病。我们讨论了未来的研究思路的基础上U-统计检验的显着性研究之间的异质性，并将工作扩展到元回归设置。版权所有（C）2011约翰威利父子有限公司
Meta-analysis is the methodology for combining findings from similar research studies asking the same question. When the question of interest involves multiple outcomes, multivariate meta-analysis is used to synthesize the outcomes simultaneously taking into account the correlation between the outcomes. Likelihood-based approaches, in particular restricted maximum likelihood (REML) method, are commonly utilized in this context. REML assumes a multivariate normal distribution for the random-effects model. This assumption is difficult to verify, especially for meta-analysis with small number of component studies. The use of REML also requires iterative estimation between parameters, needing moderately high computation time, especially when the dimension of outcomes is large. A multivariate method of moments (MMM) is available and is shown to perform equally well to REML. However, there is a lack of information on the performance of these two methods when the true data distribution is far from normality. In this paper, we propose a new nonparametric and non-iterative method for multivariate meta-analysis on the basis of the theory of U-statistic and compare the properties of these three procedures under both normal and skewed data through simulation studies. It is shown that the effect on estimates from REML because of non-normal data distribution is marginal and that the estimates from MMM and U-statistic-based approaches are very similar. Therefore, we conclude that for performing multivariate meta-analysis, the U-statistic estimation procedure is a viable alternative to REML and MMM. Easy implementation of all three methods are illustrated by their application to data from two published meta-analysis from the fields of hip fracture and periodontal disease. We discuss ideas for future research based on U-statistic for testing significance of between-study heterogeneity and for extending the work to meta-regression setting. Copyright (C) 2011 John Wiley & Sons, Ltd.