A0490
Title: Balancing utility and cost in dynamic treatment regimes
Authors: Yuqian Zhang - Renmin University of China (China) [presenting]
Kai Chen - Renmin University of China (China)
Abstract: Dynamic treatment regimes (DTRs) are personalized, adaptive strategies designed to guide the sequential allocation of treatments based on individual characteristics over time. Before each treatment assignment, covariate information is collected to refine treatment decisions and enhance their effectiveness. The more information is gathered, the more precise the decisions can be. However, this also leads to higher costs during the data collection phase. A balanced Q-learning method is proposed that strikes a balance between the utility of the DTR and the costs associated with both treatment assignment and covariate assessment. The performance of the proposed method is demonstrated through extensive numerical studies, including simulations and a real-data application to the MIMIC-III dataset.
