$Q$- and $A$-Learning Methods for Estimating Optimal Dynamic Treatment Regimes
This work addresses the challenge of sequential treatment decision-making in clinical practice for physicians and patients, but it appears incremental as it reviews and applies established methods.
The paper tackles the problem of estimating optimal dynamic treatment regimes from existing clinical data, using Q- and A-learning methods, and demonstrates their application with data from a depression study.
In clinical practice, physicians make a series of treatment decisions over the course of a patient's disease based on his/her baseline and evolving characteristics. A dynamic treatment regime is a set of sequential decision rules that operationalizes this process. Each rule corresponds to a decision point and dictates the next treatment action based on the accrued information. Using existing data, a key goal is estimating the optimal regime, that, if followed by the patient population, would yield the most favorable outcome on average. Q- and A-learning are two main approaches for this purpose. We provide a detailed account of these methods, study their performance, and illustrate them using data from a depression study.