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Activity Number: 283
Type: Invited
Date/Time: Tuesday, August 2, 2016 : 8:30 AM to 10:20 AM
Sponsor: ENAR
Abstract #318206 View Presentation
Title: Stabilized Dynamic Treatment Regimes
Author(s): Yingqi Zhao* and Ruoqing Zhu and Guanhua Chen
Companies: Fred Hutchinson Cancer Research Center and University of Illinois at Urbana-Champaign and Vanderbilt University
Keywords: Dynamic treatment regimes ; Personalized medicine ; Stabilization ; Statistical learning

Dynamic treatment regimes (DTRs) are sequential decision rules for individual patients that can adapt over time to an evolving illness. The goal is to find the DTRs tailored to individual characteristics that lead to the best long term outcome if implemented. To facilitate implementation, it is important that the DTRs are easy to interpret. If a variable is important, it should contribute to the decision rule at all stages, and the effects should be consistent over stages. We introduce a general learning framework on stabilized dynamic treatment regimes (SDTRs), where we can make stabilized decisions over time using the repeated measured information on the same variable. The proposed method is based on directly maximizing an estimator of the expected long-term outcome over all constrained DTRs. Experimental studies showed a superior performance of the proposed method.

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

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