Meta-analysis, the statistical procedure for combining results from multiple independent studies, has been widely used in medical research to evaluate intervention efficacy and drug safety. In many practical situations, treatment effects vary notably among the collected studies, and the variation, often modeled by the between-study variance parameter τ, can greatly affect the inference of the overall effect size. In the past, comparative studies have been conducted for both point and interval estimation of τ. However, most are incomplete, only including a limited subset of existing methods, and some are outdated. Further, none of the studies covers descriptive measures for assessing the level of heterogeneity. Nor are they focused on rare binary events that require special attention. We summarize by far the most comprehensive set including 11 descriptive measures, 23 estimators, and 16 confidence intervals. In addition to providing synthesized information, we further categorize these methods according to their key features. We then evaluate their performance based on simulation studies that examine various realistic scenarios for rare binary events, with an illustration using a data example of a gestational diabetes meta-analysis. We conclude that there is no uniformly “best” method. However, methods with consistently better performance do exist in the context of rare binary events, and we provide practical guidelines based on numerical evidences.

中文翻译：

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荟萃分析中量化研究间异质性的统计方法，重点关注罕见二元事件


荟萃分析是结合多项独立研究结果的统计程序，已广泛应用于医学研究中，以评估干预效果和药物安全性。在许多实际情况中，所收集的研究之间的治疗效果差异显着，并且通常由研究间方差参数 τ 建模的变化可以极大地影响总体效果大小的推断。过去，对τ的点估计和区间估计都进行了比较研究。然而，大多数都是不完整的，仅包括现有方法的有限子集，并且有些已经过时。此外，没有一项研究涵盖评估异质性水平的描述性措施。他们也不关注需要特别关注的罕见双星事件。我们总结了迄今为止最全面的集合，包括 11 个描述性度量、23 个估计量和 16 个置信区间。除了提供综合信息之外，我们还根据这些方法的关键特征对它们进行进一步分类。然后，我们根据模拟研究评估它们的表现，这些研究检查罕见二元事件的各种现实场景，并使用妊娠糖尿病荟萃分析的数据示例进行说明。我们的结论是，不存在统一的“最佳”方法。然而，在罕见的二元事件的背景下确实存在具有一贯更好性能的方法，并且我们提供基于数值证据的实用指南。