Reinforcement Learning Strategies for Clinical Trials in Non-Small Cell Lung Cancer

• Activity Number: 423
• Type: Contributed
• Date/Time: Tuesday, August 2, 2011 : 2:00 PM to 3:50 PM
• Sponsor: Biopharmaceutical Section
• Author(s): Yufan Zhao*+
• Companies: Amgen Inc.
• Keywords: reinforcement learning ; Q-learning ; support vector regression ; clinical trials ; personalized medicine ; non-small cell lung cancer

Abstract

We present a reinforcement learning design to discover optimal individualized treatment regimens for a non-small cell lung cancer trial. In addition to the complexity of the problem of selecting optimal compounds for first and second-line treatments based on prognostic factors, another primary scientific goal is to determine the optimal time to initiate second-line therapy, either immediately or delayed after induction therapy, yielding the longest overall survival time. Q-learning is utilized and approximating the Q-function with time-indexed parameters can be achieved by using support vector regressions. A simulation study shows that the procedure not only successfully identifies optimal strategies of two lines treatment from clinical data, but also reliably selects the best time to initial second-line therapy while taking into account heterogeneities of NSCLC across patients.