In a randomized study, leveraging covariates related to the outcome (e.g. disease status) may produce less variable estimates of the effect of exposure. For contagion processes operating on a contact network, transmission can only occur through ties that connect affected and unaffected individuals; the outcome of such a process is known to depend intimately on the structure of the network. In this paper, we investigate the use of contact network features as efficiency covariates in exposure effect estimation. Using augmented generalized estimating equations (GEE), we estimate how gains in efficiency depend on the network structure and spread of the contagious agent or behavior. We apply this approach to simulated randomized trials using a stochastic compartmental contagion model on a collection of model-based contact networks and compare the bias, power, and variance of the estimated exposure effects using an assortment of network covariate adjustment strategies. We also demonstrate the use of network-augmented GEEs on a clustered randomized trial evaluating the effects of wastewater monitoring on COVID-19 cases in residential buildings at the the University of California San Diego.

翻译：在随机研究中，利用与结局（如疾病状态）相关的协变量可降低暴露效应估计的变异性。对于在接触网络上运行的传染病过程，传播仅能通过连接感染者与未感染者的纽带发生；此类过程的结局与网络结构密切相关。本文研究了将接触网络特征作为效率协变量用于暴露效应估计的可行性。通过增广广义估计方程（GEE），我们量化了效率增益如何随网络结构与病原体或行为传播范围变化。在基于模型构建的接触网络集合上，采用随机区室传染病模型模拟随机试验，比较不同网络协变量调整策略下暴露效应估计的偏差、统计功效与方差。我们同时展示了网络增广GEE在加州大学圣地亚哥分校住宅楼群废水监测对COVID-19病例影响的分组随机试验评估中的应用。