Cluster-randomized trials (CRTs) of infectious disease preventions often yield correlated, interval-censored data: dependencies may exist between observations from the same cluster, and event occurrence may be assessed only at intermittent study visits. This data structure must be accounted for when conducting interim monitoring and futility assessment for CRTs. In this article, we propose a flexible framework for conditional power estimation when outcomes are correlated and interval-censored. Under the assumption that the survival times follow a shared frailty model, we first characterize the correspondence between the marginal and cluster-conditional survival functions, and then use this relationship to semiparametrically estimate the cluster-specific survival distributions from the available interim data. We incorporate assumptions about changes to the event process over the remainder of the trial—as well as estimates of the dependency among observations in the same cluster—to extend these survival curves through the end of the study. Based on these projected survival functions, we generate correlated interval-censored observations, and then calculate the conditional power as the proportion of times (across multiple full-data generation steps) that the null hypothesis of no treatment effect is rejected. We evaluate the performance of the proposed method through extensive simulation studies, and illustrate its use on a large cluster-randomized HIV prevention trial.

中文翻译：

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具有区间删失端点的集群随机试验的条件功效估计

传染病预防的集群随机试验 (CRT) 通常会产生相关的、间隔删失的数据：来自同一集群的观察之间可能存在依赖关系，并且事件发生可能仅在间歇性研究访问时进行评估。在对 CRT 进行临时监控和无用性评估时，必须考虑这种数据结构。在本文中，我们提出了一个灵活的框架，用于在结果相关且经过区间审查时进行条件功率估计。在生存时间遵循共享脆弱模型的假设下，我们首先描述边缘和集群条件生存函数之间的对应关系，然后使用这种关系从可用的临时数据中半参数估计集群特定的生存分布。我们结合了关于试验剩余时间内事件过程变化的假设——以及对同一集群中观察值之间依赖性的估计——以将这些生存曲线延伸到研究结束。基于这些预计的生存函数，我们生成相关的区间删失观察，然后将条件功效计算为拒绝无治疗效果的原假设的次数比例（跨多个完整数据生成步骤）。我们通过广泛的模拟研究评估所提出方法的性能，并说明其在大型集群随机 HIV 预防试验中的用途。