Abstract Number:
3029
Submission Type:
Contributed Abstract
Contributed Abstract Type:
Paper
Participants:
Indrabati Bhattacharya (1), Ashkan Ertefaie (2), Kevin Lynch (3), James McKay (3), Brent Johnson (4)
Institutions:
(1) Florida State University, N/A, (2) University of Rochester, N/A, (3) University of Pennsylvania, N/A, (4) University of Rochester-Medical Center, N/A
Co-Author(s):
First Author:
Presenting Author:
Abstract Text:
Existing methods for estimation of dynamic treatment regimes are limited to intention-to-treat analyses--which estimate the effect of randomization to a particular treatment regime without considering the compliance behavior of patients. In this article, we propose a novel nonparametric Bayesian Q-learning approach to construct optimal sequential treatment regimes that adjust for partial compliance. We consider the popular potential compliance framework, where some potential compliances are latent and need to be imputed. The key challenge is learning the joint distribution of the potential compliances, which we accomplish using a Dirichlet process mixture model. Our approach provides two kinds of treatment regimes: (1) conditional regimes that depend on the potential compliance values; and (2) marginal regimes where the potential compliances are marginalized. Extensive simulation studies highlight the usefulness of our method compared to intention-to-treat analyses. We apply our method on the Adaptive Treatment for Alcohol and Cocaine Dependence Study (ENGAGE), where the goal is to construct optimal treatment regimes to engage patients in therapy.
Keywords:
Dirichlet process mixture|Endogeneity|Gaussian copula|Intention-to-treat analysis| |
Sponsors:
Biometrics Section
Tracks:
Personalized/Precision Medicine
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