Comparison of estimated personalized treatment rules under varying interference structures and treatment rates using dynamic weighted ordinary least squares

Abstract

Dynamic treatment regimes (DTRs) are statistical methods that use patient-specific information to estimate an optimal sequence of treatments, with the end goal of maximizing a desired outcome of interest. DTRs have many useful applications to chronic disease management when patient characteristics are monitored over multiple periods of follow up to determine whether to continue a course of treatment or adopt an alternate strategy. Applications of DTRs to interference networks, or settings where an individual’s outcome can be affected by the treatments received by other individuals in the network, have received relatively little attention in the literature. Previous work has established DTR estimation for couples in households and in networks where general forms of interference are taking place, but the extent to which interference needs to be accounted for to avoid biased estimates of optimal treatment rules remains unknown. In this paper, we demonstrate how one such DTR method, dynamic weighted ordinary least squares regression (dWOLS), can be modified to account for both within- and between-group interference in nested hierarchical networks, and we compare our implementation with other dWOLS approaches with mis-specified exposure mappings. Our findings show that both the overall rate of treatment and the proportion of individuals experiencing interference from assigned neighbours can affect the performance of dWOLS regardless of the specified exposure mapping. Although standard dWOLS may be acceptable in some settings when the overall rate of treatment and proportion of egos assigned alters is expected to be low, our results demonstrate the improved performance of a dWOLS implementation that accounts for both within- and between-group interference, regardless of the overall rate of treatment or proportion of egos assigned alters.