Wang Bingkai, Dufault Suzanne M, Small Dylan S, Jewell Nicholas P
Department of Statistics and Data Science, The Wharton School, University of Pennsylvania.
Division of Pulmonary and Critical Care Medicine, University of California, San Francisco.
Ann Appl Stat. 2023 Jun;17(2):1592-1614. doi: 10.1214/22-aoas1684. Epub 2023 May 1.
In 2019, the World Health Organization identified dengue as one of the top 10 global health threats. For the control of dengue, the Applying to Eliminate Dengue (AWED) study group conducted a cluster-randomized trial in Yogyakarta, Indonesia, and used a novel design, called the cluster-randomized test-negative design (CR-TND). This design can yield valid statistical inference with data collected by a passive surveillance system and thus has the advantage of cost-efficiency compared to traditional cluster-randomized trials. We investigate the statistical assumptions and properties of CR-TND under a randomization inference framework, which is known to be robust for small-sample problems. We find that, when the differential healthcare-seeking behavior comparing intervention and control varies across clusters (in contrast to the setting of Dufault and Jewell ( (2020a) 1429-1439) where the differential healthcare-seeking behavior is constant across clusters), current analysis methods for CR-TND can be biased and have inflated type I error. We propose the log-contrast estimator that can eliminate such bias and improve precision by adjusting for covariates. Furthermore, we extend our methods to handle partial intervention compliance and a stepped-wedge design, both of which appear frequently in cluster-randomized trials. Finally, we demonstrate our results by simulation studies and reanalysis of the AWED study.
2019年，世界卫生组织将登革热列为全球十大健康威胁之一。为了控制登革热，“应用消除登革热”（AWED）研究小组在印度尼西亚日惹进行了一项整群随机试验，并采用了一种名为整群随机检验阴性设计（CR-TND）的新颖设计。这种设计可以利用被动监测系统收集的数据得出有效的统计推断，因此与传统的整群随机试验相比具有成本效益优势。我们在随机化推断框架下研究CR-TND的统计假设和性质，已知该框架对小样本问题具有稳健性。我们发现，当比较干预组和对照组的差异就医行为在各群组间有所不同时（与Dufault和Jewell（2020a）1429 - 1439的设定相反，其差异就医行为在各群组间是恒定的），当前用于CR-TND的分析方法可能会产生偏差且第一类错误率会膨胀。我们提出了对数对比估计量，它可以通过调整协变量来消除这种偏差并提高精度。此外，我们扩展了我们的方法以处理部分干预依从性和阶梯楔形设计，这两者在整群随机试验中都经常出现。最后，我们通过模拟研究和对AWED研究的重新分析来展示我们的结果。
