A0345
Title: Fusing individualized treatment rules using auxiliary outcomes
Authors: Donglin Zeng - University of Michigan (United States) [presenting]
Abstract: An individualized treatment rule (ITR) is a decision rule that recommends treatments for patients based on their covariates. In practice, the optimal ITR that maximizes its associated value function is also expected to cause little harm to other non-primary outcomes. Hence, one goal is to learn the ITR that not only maximizes the value function for the primary outcome but also approximates the optimal rule for the other auxiliary outcomes as closely as possible. A fusion penalty is proposed to encourage ITRs based on the primary outcome and auxiliary outcomes to yield similar recommendations. A surrogate loss function is then optimized using empirical data for estimation. The non-asymptotic properties are derived for the proposed method and show that the agreement rate between the estimated ITRs for primary and auxiliary outcomes converges faster to the true agreement rate than methods without using auxiliary outcomes. Finally, simulation studies and a real data example are used to demonstrate the finite-sample performance of the proposed method.
