This is the program for the 2010 Joint Statistical Meetings in Vancouver, British Columbia.

Abstract Details

Activity Number: 274
Type: Invited
Date/Time: Tuesday, August 3, 2010 : 8:30 AM to 10:20 AM
Sponsor: General Methodology
Abstract - #306027
Title: Personalized Medicine Trials in Non-Small Cell Lung Cancer
Author(s): Michael Kosorok*+ and Yufan Zhao and Donglin Zeng and Mark Socinski
Companies: The University of North Carolina at Chapel Hill and Amgen Inc. and The University of North Carolina at Chapel Hill and The University of North Carolina at Chapel Hill
Address: , , NC, ,
Keywords: Cancer ; Censored data ; Clinical Trials ; Personalized medicine ; Reinforcement learning ; Support vector regression
Abstract:

Typical regimens for advanced metastatic stage IIIB/IV non-small cell lung cancer (NSCLC) usually consist of two lines of treatment. We present an adaptive reinforcement learning approach to discover personalized dynamic treatment regimes from a specially designed randomized clinical trial for patients with advanced NSCLC who have not been treated previously with systemic therapy. The goal is to be able to decide for each patient which of several treatments is best for each line of therapy. In addition, we wish to determine the optimal time to initiate the second line of therapy. A reinforcement learning method called Q-learning is utilized in combination with a version of support vector regression which can be applied to right-censored time-to-event data. Simulation studies show that the procedure can successfully identify optimal personalized treatment regimens.


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