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.Comment: Substantial revisio