The increasingly widespread use of meta-analysis has led to growing interest in meta-analytic methods for rare events and sparse data. Conventional approaches tend to perform very poorly in such settings. Recent work in this area has provided options for sparse data, but these are still often hampered when heterogeneity across the available studies differs based on treatment group. We propose a permutation-based approach based on conditional logistic regression that accommodates this common contingency, providing more reliable statistical tests when such patterns of heterogeneity are observed. We find that commonly used methods can yield highly inflated Type I error rates, low confidence interval coverage, and bias when events are rare and non-negligible heterogeneity is present. Our method often produces much lower Type I error rates and higher confidence interval coverage than traditional methods in these circumstances. We illustrate the utility of our method by comparing it to several other methods via a simulation study and analyzing an example data set, which assess the use of antibiotics to prevent acute rheumatic fever.

中文翻译：

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一种基于排列的罕见事件异质荟萃分析方法

元分析的日益广泛使用导致人们对罕见事件和稀疏数据的元分析方法越来越感兴趣。在这种情况下，传统方法的性能往往很差。该领域的最新工作为稀疏数据提供了选择，但当可用研究之间的异质性因治疗组而异时，这些选择仍然经常受到阻碍。我们提出了一种基于条件逻辑回归的基于排列的方法，该方法适应这种常见的偶然性，在观察到这种异质性模式时提供更可靠的统计测试。我们发现，当事件罕见且存在不可忽略的异质性时，常用方法会产生高度夸大的 I 类错误率、低置信区间覆盖率和偏差。在这些情况下，我们的方法通常会产生比传统方法低得多的 I 类错误率和更高的置信区间覆盖率。我们通过模拟研究将其与其他几种方法进行比较并分析示例数据集来说明我们的方法的实用性，这些数据集评估了使用抗生素预防急性风湿热。