Introductory Overview Lecture 6: Personalized Medicine

• Activity Number: 275
• Type: Other
• Date/Time: Tuesday, August 6, 2013 : 8:30 AM to 10:20 AM
• Sponsor: ASA
• Author(s): Anastasios (Butch) Tsiatis*+
• Companies: North Carolina State University
• Keywords: optimal treatment ; dobly robust estimator ; clinical trial ; Q-learning ; dynamic treatment regime

Abstract

In the arena of medical care, a goal is to identify the optimal treatment regime; that is, the regime that, if followed by the entire population of patients, would lead to the best outcome on average. Given data from a clinical trial or observational study, we consider finding the optimal regime within a specified class by finding the regime that optimizes an estimator of overall population mean outcome. To take into account possible confounding in an observational study and to increase precision, we use a doubly robust augmented inverse probability weighted estimator for this purpose. Simulations and application to data from a breast cancer clinical trial demonstrate the performance of the method.

When considering more than one decision point treatment decisions at any decision point may be based on all the accruing information on the patient up to that point. Rules that assign treatments at different points in time as a function of accruing information are referred to as dynamic treatment regimes. We will discuss how dynamic programming can be used to derive such optimal dynamic treatment regimes using Q-learning.