B1744
Title: Estimating and improving individualized treatment rules and dynamic treatment regimes with an instrumental variable
Authors: Bo Zhang - Fred Hutchinson Cancer Center (United States) [presenting]
Abstract: Estimating individualized treatment rules (ITRs) and dynamic treatment regimes (DTRs) from retrospective observational data is challenging as some degree of unmeasured confounding is often expected. We develop a framework for estimating properly defined ``optimal'' ITRs and DTRs with a possibly time-varying instrumental variable (IV) when unmeasured covariates confound the treatment and outcome, rendering the potential outcome distributions possibly partially identified. We define a generic class of estimands (termed IV-optimal ITRs/DTRs)and study the associated estimation problem. We then extend the IV-optimality framework to tackle the policy improvement problem, delivering IV-improved ITRs/DTRs that are guaranteed to perform no worse and potentially better than a pre-specified baseline ITR/DTR. Importantly, our IV-improvement framework opens up the possibility of strictly improving upon DTRs that are optimal under the no unmeasured confounding assumption (NUCA). We demonstrate via extensive simulations the superior performance of IV-optimal and IV-improved ITRs/DTRs over the ITRs/DTRs that are optimal only under the NUCA. In a real data example, we embed retrospective observational registry data into a natural, two-stage experiment with noncompliance using a differential-distance-based, time-varying IV and estimate useful IV-optimal DTRs that assign mothers to a high-level or low-level neonatal intensive care unit based on their prognostic variables.
