Jackson Dan, Riley Richard, White Ian R
MRC Biostatistics Unit, Cambridge, U.K..
Stat Med. 2011 Sep 10;30(20):2481-98. doi: 10.1002/sim.4172. Epub 2011 Jan 26.
The multivariate random effects model is a generalization of the standard univariate model. Multivariate meta-analysis is becoming more commonly used and the techniques and related computer software, although continually under development, are now in place. In order to raise awareness of the multivariate methods, and discuss their advantages and disadvantages, we organized a one day 'Multivariate meta-analysis' event at the Royal Statistical Society. In addition to disseminating the most recent developments, we also received an abundance of comments, concerns, insights, critiques and encouragement. This article provides a balanced account of the day's discourse. By giving others the opportunity to respond to our assessment, we hope to ensure that the various view points and opinions are aired before multivariate meta-analysis simply becomes another widely used de facto method without any proper consideration of it by the medical statistics community. We describe the areas of application that multivariate meta-analysis has found, the methods available, the difficulties typically encountered and the arguments for and against the multivariate methods, using four representative but contrasting examples. We conclude that the multivariate methods can be useful, and in particular can provide estimates with better statistical properties, but also that these benefits come at the price of making more assumptions which do not result in better inference in every case. Although there is evidence that multivariate meta-analysis has considerable potential, it must be even more carefully applied than its univariate counterpart in practice.
多变量随机效应模型是标准单变量模型的推广。多变量荟萃分析的应用越来越普遍，其技术及相关计算机软件虽仍在不断发展，但已基本具备。为提高对多变量方法的认识，并讨论其优缺点，我们在皇家统计学会组织了一场为期一天的“多变量荟萃分析”活动。除了传播最新进展外，我们还收到了大量的意见、关切、见解、批评和鼓励。本文对当天的讨论进行了全面阐述。通过让其他人有机会回应我们的评估，我们希望确保在多变量荟萃分析仅仅成为另一种广泛使用的事实上的方法，而医学统计学界却未对其进行适当考量之前，各种观点和意见都能得到充分表达。我们使用四个具有代表性但相互对比的例子，描述了多变量荟萃分析已发现的应用领域、可用方法、通常遇到的困难以及支持和反对多变量方法的论据。我们得出结论，多变量方法可能是有用的，特别是可以提供具有更好统计特性的估计，但这些好处是以做出更多假设为代价的，而这些假设并非在每种情况下都能带来更好的推断。尽管有证据表明多变量荟萃分析具有相当大的潜力，但在实践中它必须比单变量荟萃分析更谨慎地应用。
