期刊
PSYCHOLOGICAL METHODS
卷 17, 期 4, 页码 478-494出版社
AMER PSYCHOLOGICAL ASSOC
DOI: 10.1037/a0029373
关键词
decision rules; adaptive interventions; Q-learning; regression
资金
- NIAAA NIH HHS [R37 AA011873, AA11873] Funding Source: Medline
- NIDA NIH HHS [P50 DA010075, P50DA010075, DA12414, R01 DA012414] Funding Source: Medline
- NIMH NIH HHS [MH069614, R01 MH080015, MH080015, R01 MH069614] Funding Source: Medline
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.
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