Personalized Dynamic Treatment Regimes in Continuous Time: A Bayesian Joint Model for Optimizing Clinical Decisions with Timing
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Accurate models of clinical actions and their impacts on disease progression are critical for estimating personalized optimal dynamic treatment regimes (DTRs) in medical/health research, especially in managing chronic conditions. Traditional statistical methods for DTRs usually focus on estimating the optimal treatment or dosage at each given medical intervention, but overlook the important question of "when this intervention should happen." We fill this gap by building a generative model for a sequence of medical interventions–which are discrete events in continuous time–with a marked temporal point process (MTPP) where the mark is the assigned treatment or dosage. This clinical action model is then embedded into a Bayesian joint framework where the other components model clinical observations including longitudinal medical measurements and time-to-event data. Moreover, we propose a policy gradient method to learn the personalized optimal clinical decision that maximizes patient survival by interacting the MTPP with the model on clinical observations while accounting for uncertainties in clinical observations. A signature application of the proposed approach is to schedule follow-up visitations and assign a dosage at each visitation for patients after kidney transplantation. We evaluate our approach with comparison to alternative methods on both simulated and real-world datasets. In our experiments, the personalized decisions made by our method turn out to be clinically useful: they are interpretable and successfully help improve patient survival. The R package doct (short for "Decisions Optimized in Continuous Time") implementing the proposed model and algorithm is available at https://github.com/YanxunXu/doct.
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