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Activity Number: 149
Type: Invited
Date/Time: Monday, August 10, 2015 : 10:30 AM to 12:20 PM
Sponsor: Section on Bayesian Statistical Science
Abstract #314335 View Presentation
Title: Subgroup-Based Adaptive (SUBA) Designs for Sequential, Multiple Assignment, Randomized Trials
Author(s): Yanxun Xu* and Peter Mueller and Peter F. Thall
Companies: The University of Texas at Austin and The University of Texas at Austin and MD Anderson Cancer Center
Keywords: Subgroup ; Bayesian ; Adaptive Design ; Dynamic Treatment Regime ; Q-Learning ; Targeted Therapy
Abstract:

Medical treatment often involves multiple cycles of therapy. Physicians routinely choose a patient's treatment in each cycle adaptively based on the patient's history of treatments and clinical outcomes. This paradigm is called dynamic treatment regime (DTR). There has been extensive research to develop or optimize DTRs in medicine. However, utilizing patients' genetic profiling to optimize each patient's DTR has not been addressed. We work on a phase I/II biomarker trial design called SUBA+SMART, in which each patient's DTR is adaptively optimized in each of multiple cycles of therapies based on the joint binary efficacy and toxicity outcomes in each cycle through a subgroup analysis using patients' biomarker information. Depending on the expression of specific prognostic biomarkers, targeted therapies assign different cancer drugs to subgroups of patients even if they are diagnosed with the same type of cancer by traditional means, such as tumor location. We first consider the case of two cycles with bivariate binary outcomes and use a Bayesian Q-learning algorithm to choose optimal treatments.


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