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Mon, Sept 11, 2017, noon:
Welcoming of Postdoctoral Fellows: Angela Bruns, Karra Greenberg, Sarah Seelye and Emily Treleaven

Q-Learning: A Data Analysis Method for Constructing Adaptive Interventions

Publication Abstract

Nahum-Shani, I., M. Qian, Daniel Almirall, W. Pelham, B. Gnagy, G. Fabiano, J. Waxmonsky, J. Yu, and Susan A. Murphy. 2012. "Q-Learning: A Data Analysis Method for Constructing Adaptive Interventions." Psychological Methods, 17(4): 478-494.

Increasing interest in individualizing and adapting intervention services over time has led to the development of adaptive interventions. Adaptive interventions operationalize the individualization of a sequence of intervention options over time via the use of decision rules that input participant information and output intervention recommendations. We introduce Q-learning, which is a generalization of regression analysis to settings in which a sequence of decisions regarding intervention options or services is made. The use of Q is to indicate that this method is used to assess the relative qualify of the intervention options. In particular, we use Q-learning with linear regression to estimate the optimal (i.e., most effective) sequence of decision rules. We illustrate how Q-teaming can be used with data from sequential multiple assignment randomized trials (SMARTs; Murphy, 2005) to inform the construction of a more deeply tailored sequence of decision rules than those embedded in the SMART design. We also discuss the advantages of Q-learning compared to other data analysis approaches. Finally, we use the Adaptive Interventions for Children With ADHD SMART study (Center for Children and Families, University at Buffalo, State University of New York, William E. Pelham as principal investigator) for illustration.

DOI:10.1037/a0029373 (Full Text)

PMCID: PMC3747013. (Pub Med Central)

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