Balancing utility and cost in dynamic treatment regimes

TL;DR

This paper introduces a balanced Q-learning approach for dynamic treatment regimes that optimally balances treatment utility with the costs of data collection and covariate assessment, demonstrated through simulations and real data.

Contribution

It proposes a novel balanced Q-learning method that explicitly incorporates cost considerations into the optimization of dynamic treatment regimes.

Findings

Effective in balancing utility and costs in simulations

Improves decision-making in real-data application

Outperforms existing methods in cost-utility trade-offs

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 we gather, the more precise our decisions can be. However, this also leads to higher costs during the data collection phase. In this work, we propose a balanced Q-learning method 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 database.