The views expressed here are those of the individual authors and not necessarily those of the JSM sponsors, their officers, or their staff.
Abstract Details
Activity Number:
|
423
|
Type:
|
Contributed
|
Date/Time:
|
Tuesday, August 2, 2011 : 2:00 PM to 3:50 PM
|
Sponsor:
|
Biopharmaceutical Section
|
Abstract - #300933 |
Title:
|
Reinforcement Learning Strategies for Clinical Trials in Non-Small Cell Lung Cancer
|
Author(s):
|
Yufan Zhao*+
|
Companies:
|
Amgen Inc.
|
Address:
|
One Amgen Drive, Thousand Oaks, CA, 91320,
|
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.
|
The address information is for the authors that have a + after their name.
Authors who are presenting talks have a * after their name.
Back to the full JSM 2011 program
|
2011 JSM Online Program Home
For information, contact jsm@amstat.org or phone (888) 231-3473.
If you have questions about the Continuing Education program, please contact the Education Department.