In a cluster randomized trial (CRT), groups of people are randomly assigned to different interventions. Existing parametric and semiparametric methods for CRTs rely on distributional assumptions or a large number of clusters to maintain nominal confidence interval (CI) coverage. Randomization-based inference is an alternative approach that is distribution-free and does not require a large number of clusters to be valid. Although it is well-known that a CI can be obtained by inverting a randomization test, this requires testing a non-zero null hypothesis, which is challenging with non-continuous and survival outcomes. In this article, we propose a general method for randomization-based CIs using individual-level data from a CRT. This approach accommodates various outcome types, can account for design features such as matching or stratification, and employs a computationally efficient algorithm. We evaluate this method's performance through simulations and apply it to the Botswana Combination Prevention Project, a large HIV prevention trial with an interval-censored time-to-event outcome.

中文翻译：

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用于整群随机试验的基于随机化的置信区间。


在整群随机试验 (CRT) 中，人群被随机分配接受不同的干预措施。现有的 CRT 参数和半参数方法依赖于分布假设或大量聚类来维持名义置信区间 (CI) 覆盖范围。基于随机化的推理是一种替代方法，它是无分布的，并且不需要大量的集群才有效。尽管众所周知，CI 可以通过反转随机化检验来获得，但这需要检验非零零假设，这对于非连续和生存结果具有挑战性。在本文中，我们提出了一种使用来自 CRT 的个体级数据的基于随机化的 CI 的通用方法。这种方法适应各种结果类型，可以考虑匹配或分层等设计特征，并采用计算高效的算法。我们通过模拟评估该方法的性能，并将其应用于博茨瓦纳组合预防项目，这是一项大型艾滋病毒预防试验，具有间隔审查的事件时间结果。