Quantitative Sciences, GlaxoSmithKline R&D, Stevenage, UK.
Stat Methods Med Res. 2013 Apr;22(2):117-32. doi: 10.1177/0962280211432218. Epub 2012 Jan 4.
This is a review of methods for the meta-analysis of incidence of rare events using summary-level data. It is motivated and illustrated by the dataset used in a published analysis of cardiovascular safety in rosiglitazone trials. This review compares available methods for binary data, considering risk-difference, relative-risk and odds-ratio scales, fixed-effect and random-effects models, and frequentist and Bayesian approaches. Particular issues in this dataset include low incidence rates, the occurrence of studies with no events under one or all treatments, and discrepancy among results achieved using different statistical methodologies. The common method of adding a correction factor to handle zeroes may introduce bias where the incidence of events is small, as in this case. Alternative analyses on the log-odds scale are shown to give similar results, but the choice between them is less important than the potential sources of bias in any meta-analysis arising from limitations in the underlying dataset. It is important to present results carefully, including numerical and graphical summaries on the natural scale of risk when the analysis is on a statistically appropriate scale such as log-odds: the incidence rates should accompany an estimated ratio (of odds or risk) to put the analysis into the proper context. Beyond the statistical methodologies which are the focus of this paper, this dataset highlights the importance of understanding the limitations of the data being combined. Because the rosiglitazone dataset contains clinically heterogeneous trials with low event rates that were not designed or intended to assess cardiovascular outcomes, the findings of any meta-analysis of such trials should be considered hypothesis-generating.
这是一篇关于使用汇总数据进行罕见事件荟萃分析方法的综述。它的动机和例证来自于一篇已发表的罗格列酮试验心血管安全性分析中使用的数据集。本综述比较了用于二进制数据的可用方法，考虑了风险差、相对风险和优势比尺度、固定效应和随机效应模型以及频率主义和贝叶斯方法。该数据集中特别的问题包括低发生率、在一种或所有治疗下都没有事件的研究发生，以及使用不同统计方法学获得的结果之间存在差异。在这种情况下，为处理零值而添加校正因子的常见方法可能会引入偏差。在对数优势尺度上进行替代分析显示出相似的结果，但在任何荟萃分析中，选择哪种方法并不重要，重要的是潜在的偏倚来源，这些偏倚源源于基础数据集中的局限性。在进行统计上适当的分析（如对数优势）时，以风险的自然尺度仔细呈现结果非常重要，包括数值和图形汇总：在将分析置于适当的背景下时，应同时报告估计的比值（优势比或风险比）。除了本文关注的统计方法学之外，该数据集还强调了理解正在组合的数据的局限性的重要性。由于罗格列酮数据集包含具有低事件率的临床异质试验，并且这些试验并非旨在评估心血管结局，因此对这些试验进行的任何荟萃分析的结果都应被视为产生假说。
