title: Optimizing patient treatment decisions in an era of rapid technological advances: the case of hepatitis C treatment.
abstract: How long should a patient with a treatable chronic disease wait for more effective treatments before accepting the best available treatment? We develop a framework to guide optimal treatment decisions for a deteriorating chronic disease when treatment technologies are improving over time. We formulate an optimal stopping problem using a discrete-time, finite-horizon Markov decision process. The goal is to maximize a patient's quality-adjusted life expectancy. We derive structural properties of the model and analytically solve a three-period treatment decision problem. We illustrate the model with the example of treatment for chronic hepatitis C virus (HCV). Chronic HCV affects 3-4 million Americans and has been historically difficult to treat, but increasingly effective treatments have been commercialized in the past few years. We show that the optimal treatment decision is more likely to be to accept currently available treatment-despite expectations for future treatment improvement-for patients who have high-risk history, who are older, or who have more comorbidities. Insights from this study can guide HCV treatment decisions for individual patients. More broadly, our model can guide treatment decisions for curable chronic diseases by finding the optimal treatment policy for individual patients in a heterogeneous population.
authors.affiliation: Department of Industrial and Systems Engineering, University of Washington, Seattle, WA, USA. liushan@uw.edu.
authors.forename: Shan
authors.initials: S
authors.lastname: Liu
authors.affiliation: Department of Management Science and Engineering, Stanford University, Stanford, CA, USA.
authors.forename: Margaret L
authors.initials: ML
authors.lastname: Brandeau
authors.affiliation: Center for Primary Care and Outcomes Research and Center for Health Policy, Stanford University, Stanford, CA, USA.
authors.forename: Jeremy D
authors.initials: JD
authors.lastname: Goldhaber-Fiebert
mesh_terms: D000998:Antiviral Agents / Q000627:therapeutic use; D020811:Biomedical Technology; D003661:Decision Support Techniques; D005260:Female; D019698:Hepatitis C, Chronic / Q000188:drug therapy / Q000628:therapy*; D006801:Humans; D008297:Male; D008390:Markov Chains; D008875:Middle Aged; D008962:Models, Theoretical; D058996:Quality Improvement; D019057:Quality-Adjusted Life Years; D018570:Risk Assessment; D016896:Treatment Outcome
chemical_list: D000998:Antiviral Agents
keywords: Decision analysis; Dynamic programming; Hepatitis C treatment; Markov decision process; Medical decision making; Technology adoption