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Activity Number: 25
Type: Topic Contributed
Date/Time: Sunday, August 9, 2015 : 2:00 PM to 3:50 PM
Sponsor: Biometrics Section
Abstract #316168 View Presentation
Title: Q-Learning Residual Analysis
Author(s): Bibhas Chakraborty* and Ashkan Ertefaie and Susan Shortreed
Companies: Duke University and University of Pennsylvania and Group Health Research Institute
Keywords: Q-learning ; residual analysis ; SMART designs
Abstract:

Q-learning is a regression-based approach that uses longitudinal data to construct dynamic treatment regimes, which are sequences of decision rules that use patient information to inform future treatment decisions. An optimal dynamic treatment regime is composed of a sequence of decision rules that indicate how to individualize treatment using the patients' baseline and time-varying characteristics to optimize the final outcome. Constructing optimal dynamic regimes using Q-learning depends heavily on the assumption that regression models at each decision point are correctly specified; yet model checking in the context of Q-learning has been overlooked in the current literature. In this article, we show that residual plots obtained from standard Q-learning models may fail to adequately check the quality of the model fit. We present a modified Q-learning procedure that accommodates residual analyses using standard tools. We present simulation studies showing the advantage of the proposed modification over standard Q-learning. We illustrate this new Q-learning approach using data collected from a sequential multiple-assignment randomized trial of patients with schizophrenia.


Authors who are presenting talks have a * after their name.

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