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

PSC In The News

RSS Feed icon

Inglehart says European social democracy is a victim of its own success

Bound, Khanna, and Morales find multiple effects of H1-B visas on US tech industry

Prescott says public criminal registries have downside

More News

Highlights

Heather Ann Thompson wins Bancroft Prize for History for 'Blood in the Water'

Michigan ranks in USN&WR top-10 grad schools for sociology, public health, labor economics, social policy, social psychology

Paula Lantz to speak at Women in Health Leadership Summit, March 24, 2:30-5:30 Michigan League

New site highlights research, data, and publications of Relationship Dynamics and Social Life study

More Highlights

Next Brown Bag

Mon, March 20, 2017, noon:
Dean Yang, Taken by Storm

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)

Browse | Search : All Pubs | Next