Primary Submission Category: Generalizability/Transportability
Targeted Optimal Treatment Regime Learning
Authors: Shu Yang,
Presenting Author: Shu Yang*
Personalized decision-making, aiming to derive optimal individualized treatment rules (ITRs) based on individual characteristics, has recently attracted increasing attention in many fields, such as medicine, social services, and economics. Current literature mainly focuses on estimating ITRs from a single source population. In real-world applications, the distribution of a target population can be different from that of the source population. Therefore, ITRs learned by existing methods may not generalize well to the target population. We consider an ITR estimation problem where the source and target populations may be heterogeneous. We develop a weighting framework that tailors an ITR for a target population. Specifically, we propose a calibrated augmented inverse probability weighted estimator of the value function for the target population and estimate an optimal ITR by maximizing this estimator within a class of pre-specified ITRs. We show that the proposed calibrated estimator is consistent and asymptotically normal even with flexible semi/nonparametric models for nuisance function approximation. The framework applies to general outcomes (including censored survival outcomes) and scenarios when the target sample provides individual covariate data or only summary statistics due to privacy concerns. We demonstrate the empirical performance of the proposed method using simulation studies and real clinical data applications.