Department of Biostatistics, Harvard University, T. H. Chan School of Public Health, 677 Huntington Ave, Boston, MA 02115, USA.
Department of Biostatistics, Harvard University, T. H. Chan School of Public Health, 677 Huntington Ave, Boston, MA 02115, USA and Department of Population Medicine, Harvard Pilgrim Health Care Institute and Harvard Medical School, 401 Park Drive, Boston, MA 02215, USA.
Biostatistics. 2021 Oct 13;22(4):913-927. doi: 10.1093/biostatistics/kxaa007.
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.
在整群随机试验 (cluster randomized trial, CRT) 中，将人群随机分为不同的干预组。现有的 CRT 参数和半参数方法依赖于分布假设或大量的群组来维持名义置信区间 (confidence interval, CI) 覆盖率。基于随机化的推断是一种替代方法，它是无分布的，并且不需要大量的群组才能有效。尽管众所周知，可以通过反转随机化检验来获得 CI，但这需要检验非零零假设，对于非连续和生存结局，这是具有挑战性的。在本文中，我们提出了一种使用 CRT 中个体水平数据进行基于随机化的 CI 的通用方法。该方法适用于各种结局类型，可以考虑匹配或分层等设计特征，并采用计算效率高的算法。我们通过模拟评估了该方法的性能，并将其应用于博茨瓦纳组合预防项目，这是一项大型 HIV 预防试验，其结局为区间删失时间事件。
