Dufault Suzanne M, Jewell Nicholas P
Division of Epidemiology and Biostatistics, University of California, Berkeley, California.
Department of Medical Statistics, London School of Hygiene & Tropical Medicine, London, United Kingdom.
Stat Med. 2020 May 15;39(10):1429-1439. doi: 10.1002/sim.8488. Epub 2020 Jan 30.
In cluster randomized trials (CRTs), the outcome of interest is often a count at the cluster level. This occurs, for example, in evaluating an intervention with the outcome being the number of infections of a disease such as HIV or dengue or the number of hospitalizations in the cluster. Standard practice analyzes these counts through cluster outcome rates using an appropriate denominator (eg, population size). However, such denominators are sometimes unknown, particularly when the counts depend on a passive community surveillance system. We consider direct comparison of the counts without knowledge of denominators, relying on randomization to balance denominators. We also focus on permutation tests to allow for small numbers of randomized clusters. However, such approaches are subject to bias when there is differential ascertainment of counts across arms, a situation that may occur in CRTs that cannot implement blinded interventions. We suggest the use of negative control counts as a method to remove, or reduce, this bias, discussing the key properties necessary for an effective negative control. A current example of such a design is the recent extension of test-negative designs to CRTs testing community-level interventions. Via simulation, we compare the performance of new and standard estimators based on CRTs with negative controls to approaches that only use the original counts. When there is no differential ascertainment by intervention arm, the count-only approaches perform comparably to those using debiasing negative controls. However, under even modest differential ascertainment, the count-only estimators are no longer reliable.
在整群随机试验（CRT）中，感兴趣的结局通常是整群水平的计数。例如，在评估一项干预措施时，结局是诸如艾滋病毒或登革热等疾病的感染数或整群中的住院数，就会出现这种情况。标准做法是通过使用适当分母（例如人口规模）的整群结局率来分析这些计数。然而，这些分母有时是未知的，特别是当计数依赖于被动社区监测系统时。我们考虑在不知道分母的情况下直接比较计数，依靠随机化来平衡分母。我们还专注于置换检验，以适用于少量随机整群。然而，当各臂的计数存在差异确定时，这种方法会产生偏差，这种情况可能发生在无法实施盲法干预的CRT中。我们建议使用阴性对照计数作为消除或减少这种偏差的方法，并讨论有效阴性对照所需的关键特性。这种设计的一个当前例子是最近将检验阴性设计扩展到测试社区层面干预措施的CRT。通过模拟，我们将基于带有阴性对照的CRT的新估计器和标准估计器的性能与仅使用原始计数的方法进行比较。当各干预臂不存在差异确定时，仅计数方法的性能与使用去偏阴性对照的方法相当。然而，即使在适度的差异确定情况下，仅计数估计器也不再可靠。
