Arends Lidia R, Vokó Zoltán, Stijnen Theo
Department of Epidemiology & Biostatistics, Erasmus University Medical School, P.O. Box 1738, 3000 DR Rotterdam, The Netherlands.
Stat Med. 2003 Apr 30;22(8):1335-53. doi: 10.1002/sim.1370.
In meta-analysis of clinical trials published in the medical literature it is customary to restrict oneself to standard univariate fixed or random effects models. If multiple endpoints are present, each endpoint is analysed separately. A few articles have been written in the statistical literature on multivariate methods for multiple outcome measures. However, these methods were not easy to apply in practice, because self-written programs had to be used, and the examples were only two-dimensional. In this paper we consider a meta-analysis on the effect on stroke-free survival of surgery compared to conservative treatment in patients with increased risk of stroke. Three summary measures per trial are available: short-term post-operative morbidity/mortality in the surgical group; long-term event rate in the surgical group, and the event rate in the conservative group. We analyse the three outcomes jointly with a general linear MIXED model, compare the results with the standard univariate approaches and discuss the many advantages of multivariate modelling. It turns out that the general linear MIXED model is a very convenient framework for multivariate meta-analysis. All analyses could be carried out in standard general linear MIXED model software.
在医学文献中发表的临床试验的荟萃分析中，通常将自己限制在标准的单变量固定或随机效应模型。如果存在多个终点，则分别分析每个终点。统计文献中已经有几篇关于多变量方法用于多个结局指标的文章。然而，这些方法在实践中并不容易应用，因为必须使用自编程序，而且例子只有二维的。在本文中，我们考虑对中风风险增加的患者进行手术与保守治疗相比对无中风生存期影响的荟萃分析。每个试验有三个汇总指标：手术组术后短期发病率/死亡率；手术组长期事件发生率，以及保守组的事件发生率。我们用一般线性混合模型联合分析这三个结局，将结果与标准单变量方法进行比较，并讨论多变量建模的诸多优点。结果表明，一般线性混合模型是多变量荟萃分析的一个非常方便的框架。所有分析都可以在标准的一般线性混合模型软件中进行。
