论文标题
对感染疾病的聚类随机试验的假设分析，以实现治疗效果和网络效应
Assumption-Lean Analysis of Cluster Randomized Trials in Infectious Diseases for Intent-to-Treat Effects and Network Effects
论文作者
论文摘要
簇随机试验（CRT）是研究传染病环境中干预效果的流行设计。但是，对CRT的标准分析主要依赖于强的参数方法，通常是混合效应模型来说明聚类结构，并着重于整体意图对治疗（ITT）效果来评估有效性。本文提出了两种假设的方法，用于分析CRT中的两种效应，ITT效应和网络效应，在众所周知的合规组中。对于ITT效应，我们研究观察到的协变量之间的总体和异质ITT效应，在我们不对群集大小施加参数模型或渐近限制。对于合规组之间的网络效应，我们提出了一种新的基于界限的方法，该方法使用预处理协变量，分类算法和线性程序来获得尖锐的界限。我们方法的一个关键特征是，随着分类算法的改进，界限可能会变得更狭窄，并且该方法也可能对使用仪器变量的部分识别进行研究。最后，我们通过重新分析了CRT研究面罩和洗手液对2008年香港大室内流感流感的影响的影响。
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 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.
