Assumption-Lean Analysis of Cluster Randomized Trials in Infectious Diseases for Intent-to-Treat Effects and Network Effects

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 mixed-effect models to account for the clustering structure, and focuses on the overall intent-to-treat (ITT) effect to evaluate effectiveness. The article 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 pretreatment 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.集群随机试验（Cluster Randomized Trials, CRTs）是在传染病研究场景中评估干预措施效果的常用研究设计。然而，现有集群随机试验的标准分析方法主要依赖强参数化框架，通常采用混合效应模型以适配集群结构，且多聚焦于整体意向治疗（Intent-to-Treat, ITT）效应以评估干预有效性。本文提出两种基于弱假设的分析方法，用于解析集群随机试验中的两类效应：意向治疗效应，以及已知依从性组别间的网络效应。针对意向治疗效应，本文在未对集群规模施加参数模型或渐近约束的前提下，探究了观测协变量下的整体意向治疗效应与异质性意向治疗效应。针对依从性组别间的网络效应，本文提出一种基于边界的新型分析方法：该方法利用预处理协变量、分类算法与线性规划，以获取精确边界。本方法的核心特性在于：随着分类算法性能提升，所得边界可进一步收窄；此外，该方法还可应用于工具变量框架下的部分识别研究。文末我们通过重新分析一项集群随机试验完成总结——该试验旨在评估口罩与手部消毒剂对2008年香港大流行间期流感传播的干预效果。