Constructing Dynamic Treatment Regimes in Infinite-Horizon Settings
By Ashkan Ertefaie. 2014
21 citations recorded by the source corpus.
The application of existing methods for constructing optimal dynamic treatment regimes is limited to cases where investigators are interested in optimizing a utility function over a fixed period of time (finite horizon). In this manuscript, we develop an inferential procedure based on temporal difference residuals for optimal dynamic treatment regimes in infinite-horizon settings, where there is no a priori fixed end of follow-up point. The proposed method can be used to determine the optimal regime in chronic diseases where patients are monitored and treated throughout their life. We derive large sample results necessary for conducting inference. We also simulate a cohort of patients with diabetes to mimic the third wave of the National Health and Nutrition Examination Survey, and we examine the performance of the proposed method in controlling the level of hemoglobin A1c. Supplementary materials for this article are available online.
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- Nathan Kallus
- Masatoshi Uehara
- Michael R. Kosorok
- Eric B. Laber
- Daniel J. Luckett
- A. Kahkoska
- David M. Maahs
- E. Mayer‐Davis
Citations in the corpus
- Efficiently Breaking the Curse of Horizon in Off-Policy Evaluation with Double Reinforcement Learning (2019, positive): “Although we do not explicitly use counterfactual notation (potential outcomes or do -calculus), if we assume the usual sequential ignorability conditions (Ertefaie, 2014), the estimands are the same and our results immediately apply.”
- Precision Medicine. (2019, methodology): “Q-learning in the infinite horizon setting can be derived using analogous arguments (see Ertefaie 2015) and has been applied to manage infection from Pseudomonas Annu Rev Stat Appl.”
- Estimating Dynamic Treatment Regimes in Mobile Health Using V-learning (2016, background): “Ertefaie (2014) introduced GGQ for estimating dynamic treatment regimes in infinite horizon settings (see also Maei et al. 2010; Murphy et al. 2016).”