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Activity Number: 83
Type: Invited
Date/Time: Sunday, July 30, 2017 : 8:30 PM to 10:30 PM
Sponsor: Section on Statistical Learning and Data Science
Abstract #323241
Title: Optimal Dynamic Treatment Regimes Using Decision Lists
Author(s): Yichi Zhang* and Eric Laber and Anastasios (Butch) Tsiatis and Marie Davidian
Companies: Harvard University and North Carolina State University and North Carolina State University and North Carolina State University
Keywords: Dynamic treatment regimes ; Precision Medicine ; Decision Lists ; Q-learning

A dynamic treatment regime (DTR) formalizes precision medicine as a series of functions over decision points. At each decision point, it takes the available information of a patient as input and outputs a recommended treatment for that patient. A high-quality DTR tailors treatment decisions to individual patient as illness evolves, and thus improves patient outcomes while reducing cost and treatment burden. To facilitate meaningful information exchange during the development of DTRs, it is important that the estimated DTR be interpretable in a subject-matter context. We propose a simple, yet flexible class of DTRs whose members are representable as a short list of if-then statements. DTRs in this class are immediately interpretable and are therefore appealing choices for broad applications in practice. We develop a nonparametric Q-learning procedure to estimate the optimal DTR within this class. We establish its consistency and rate of convergence. We demonstrate the performance of the proposed method using simulations and a clinical dataset.

Authors who are presenting talks have a * after their name.

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