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

PSC In The News

RSS Feed icon

Prescott says online option for access to court system can help equalize justice

Hall et al find mixed correlations between religious affiliation and views on reproductive health coverage among women

Bloome comments on Moynihan's controversial 1965 call for national action to strengthen black families

Highlights

U-M ranked #1 in Sociology of Population by USN&WR's "Best Graduate Schools"

PAA 2015 Annual Meeting: Preliminary program and list of UM participants

ISR addition wins LEED Gold Certification

PSC Fall 2014 Newsletter now available

Next Brown Bag

Mon, March 23
Lundberg, State Care of the Elderly & Labor Supply of Adult Children

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