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  • Book
    Shan Liu.
    How can chronically ill patients make the best treatment decisions when there is uncertainty about the costs and effectiveness of new and emerging treatments? We investigate this question by evaluating new medical technologies and interventions focusing on chronic hepatitis C virus (HCV) infection. Chronic HCV affects approximately 3 million Americans and has been historically difficult to treat. New and emerging treatments show great promise in providing better health outcomes, but at a significantly higher cost. Using decision-analytic Markov models, we first examine the cost-effectiveness of various disease monitoring strategies before treatment initiation, and the improved use of a new genetic marker-guided therapy to target HCV treatment to patients. We then investigate the cost-effectiveness of population screening policies to detect and treat the estimated 2 million Americans who are unaware of their chronic HCV infection. Motivated by this applied work, we consider the general theoretical question of how long a patient with a treatable chronic disease should wait for more effective treatments to emerge before undergoing currently available treatment. This decision involves a difficult tradeoff between the deterioration of a patient's health and the magnitude of technological improvement over time. We model the patient-level treatment adoption decision problem as an optimal stopping problem using a discrete-time, finite-horizon Markov Decision Process framework. We present structural properties of the model, analytical results, and a numerical example for chronic HCV treatment. Results of this work can inform both individuals and organizations in making treatment decisions in the presence of rapid medical technology advancement.