This article focuses on meta‐analysis of low event‐rate binomial trials. We introduce two forms of random effects: (1) ‘studies at random’ (SR), where we assume no more than independence between studies; and (2) ‘effects at random’ (ER), which forces the effect size distribution to be independent of the study design. On the basis of the summary estimates of proportions, we present both unweighted and study‐size weighted methods, which, under SR, target different population parameters. We demonstrate mechanistically that the popular DerSimonian–Laird (DL) method, as DL actually warned in their paper, should never be used in this setting. We conducted a survey of the major cardiovascular literature on low event‐rate studies and found that DL using odds ratios or relative risks to be the clear method of choice. We looked at two high profile examples from diabetes and cancer, respectively, where the choice of weighted versus unweighted methods makes a large difference. A large simulation study supports the accuracy of the coverage of our approximate confidence intervals. We recommend that before looking at their data, users should prespecify which target parameter they intend to estimate (weighted vs. unweighted) but estimate the other as a secondary analysis. Copyright © 2012 John Wiley & Sons, Ltd.

中文翻译：

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低事件率二项式试验安全性的荟萃分析。

本文重点介绍低事件率二项式试验的荟萃分析。我们引入了两种形式的随机效应：（1）“随机研究”（SR），我们假设研究之间不超过独立性；(2)“随机效应”(ER)，这迫使效应大小分布独立于研究设计。在对比例的总结估计的基础上，我们提出了未加权和研究规模加权的方法，在 SR 下，它们针对不同的人口参数。我们机械地证明了流行的 DerSimonian-Laird (DL) 方法，正如 DL 在他们的论文中实际警告的那样，永远不应该在这种情况下使用。我们对关于低事件率研究的主要心血管文献进行了一项调查，发现使用优势比或相对风险的 DL 是明确的选择方法。与未加权的方法有很大的不同。大型模拟研究支持我们的近似置信区间覆盖的准确性。我们建议在查看他们的数据之前，用户应该预先指定他们打算估计的目标参数（加权与未加权），但将另一个估计为次要分析。版权所有 © 2012 John Wiley & Sons, Ltd.