Shuster Jonathan J, Jones Lynn S, Salmon Daniel A
Department of Epidemiology and Health Policy Research, College of Medicine, University of Florida, Gainesville, FL 32610, USA.
Stat Med. 2007 Oct 30;26(24):4375-85. doi: 10.1002/sim.3060.
Meta-analyses can be powerful tools to combine the results of randomized clinical trials and observational studies to make consensus inferences about a medical issue. It will be demonstrated that a common practice of testing for homogeneity of effect size, and acting upon the inference to decide between fixed vs random effects, can lead to potentially misleading results. A by-product of this paper is a new ratio estimator approach to random effects meta-analysis of a large set of studies with low event rates. As a case study, we shall use the recent Rosiglitazone example, where diagnostic testing failed to reject homogeneity, leading the investigators to use fixed effects. The results for the fixed and random effects analyses are discordant. In the fixed (random) effects analysis, the p-values for myocardial infarction were 0.03 (0.11) while those for cardiac death were 0.06 (0.0017). Had the fixed effects analysis controlled the study error for multiple testing via a Bonferonni correction, the joint 95+ per cent confidence rectangle for the two outcomes would have included odds ratios of (1.0, 1.0). For the Rosiglitazone example, random effects analysis, where all studies receive the same weight, is the superior choice over fixed effects, where two large studies dominate.
荟萃分析是一种强大的工具，可将随机临床试验和观察性研究的结果结合起来，就医学问题得出共识性推断。结果表明，检验效应量同质性并根据推断结果在固定效应与随机效应之间做出选择的常见做法，可能会导致潜在的误导性结果。本文的一个附带成果是一种新的比率估计方法，用于对大量低事件发生率研究进行随机效应荟萃分析。作为一个案例研究，我们将使用最近的罗格列酮示例，在该示例中，诊断性检验未能拒绝同质性，导致研究人员使用固定效应。固定效应分析和随机效应分析的结果不一致。在固定（随机）效应分析中，心肌梗死的p值为0.03（0.11），而心源性死亡的p值为0.06（0.0017）。如果固定效应分析通过Bonferonni校正控制多重检验的研究误差，那么这两个结果的联合95%以上置信矩形将包含比值比（1.0，1.0）。对于罗格列酮示例，随机效应分析（所有研究权重相同）比固定效应分析（两项大型研究占主导）更具优势。
