Cluster randomized trials (CRTs) are a popular design to study the effect of interventions in infectious disease settings. However, standard analysis of CRTs primarily relies on strong parametric methods, usually a Normal mixed effect models to account for the clustering structure, and focus on the overall intent-to-treat (ITT) effect to evaluate effectiveness. The paper presents two assumption-lean methods to analyze two types of effects in CRTs, ITT effects and network effects among well-known compliance groups. For the ITT effects, we study the overall and the heterogeneous ITT effects among the observed covariates where we do not impose parametric models or asymptotic restrictions on cluster size. For the network effects among compliance groups, we propose a new bound-based method that uses pre-treatment covariates, classification algorithms, and a linear program to obtain sharp bounds. A key feature of our method is that the bounds can become narrower as the classification algorithm improves and the method may also be useful for studies of partial identification with instrumental variables. We conclude by reanalyzing a CRT studying the effect of face masks and hand sanitizers on transmission of 2008 interpandemic influenza in Hong Kong.
翻译:聚类随机试验(CRTs)是研究传染性疾病干预效果的流行设计。然而,对聚类试验的标准分析主要依赖强力参数学方法,通常是用于计算聚类结构的正常混合效应模型,侧重于总体意图-治疗效应(ITT),以评价有效性。本文介绍了两种假设-原始方法,以分析两种类型的CRTs效应、ITT效应和众所周知的遵守团体之间的网络效应。对于ITT效应,我们研究观察到的未对聚类规模施加参数模型或无药限制的共变变量之间ITT效应的总体和差异性。对于遵守团体的网络效应,我们提出了一种新的基于约束性方法,即使用预处理共变法、分类算法和线性程序来获取锐利的界限。我们方法的一个主要特征是,随着分类算法的改进,界限会变得更窄,而且该方法也可能有助于研究与工具变量进行部分识别。我们的结论是,通过对CRT公司研究面罩和HHMLM在2008年香港内部输入系统的影响进行再分析。