Home > Publications . Search All . Browse All . Country . Browse PSC Pubs . PSC Report Series

PSC In The News

RSS Feed icon

Smock cited in story on how low marriage rates may exacerbate marriage-status economic inequality

Shapiro says Americans' seemingly volatile spending pattern linked to 'sensible cash management'

Work of Cigolle, Ofstedal et al. cited in Forbes story on frailty risk among the elderly

Highlights

Susan Murphy named Distinguished University Professor

Sarah Burgard and former PSC trainee Jennifer Ailshire win ASA award for paper

James Jackson to be appointed to NSF's National Science Board

ISR's program in Society, Population, and Environment (SPE) focuses on social change and social issues worldwide.

Next Brown Bag


PSC Brown Bags will return in the fall

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

Publication Abstract

Nahum-Shani, I., M. Qian, D. 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)

Browse | Search : All Pubs | Next