Q-learning: a data analysis method for constructing adaptive interventions

Inbal Nahum-Shani, Min Qian, Daniel Almirall, William E Pelham, Beth Gnagy, Gregory A Fabiano, James G Waxmonsky, Jihnhee Yu, Susan A Murphy, Inbal Nahum-Shani, Min Qian, Daniel Almirall, William E Pelham, Beth Gnagy, Gregory A Fabiano, James G Waxmonsky, Jihnhee Yu, Susan A Murphy

Abstract

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 quality 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-learning 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.

PsycINFO Database Record (c) 2013 APA, all rights reserved

Figures

Figure 1
Figure 1
Illustration of unmeasured confounders affecting O2 and Y.
Figure 2
Figure 2
Distribution of estimated coefficient [θ^1+12(∣θ^3+θ^4∣−∣θ^3−θ^4∣)]..
Figure 3
Figure 3
Distribution of estimated coefficient of (α^11).
Figure 4
Figure 4
Sequential Multiple Assignment Randomized Trial for ADHD study.
Figure 5
Figure 5
Distribution for % days on behavioral intervention for those assigned to low-intensity behavioral intervention at the first stage of the intervention.
Figure 6
Figure 6
Distribution for % days on medication for those assigned to low dose of medication at the first stage of the intervention.
Figure 7
Figure 7
Predicted mean of classroom performance for each of the second-stage intervention options (A2), given the first-stage intervention (A1) and adherence to first-stage intervention (O22).
Figure 8
Figure 8
Predicted estimated quality of the second-stage intervention for each of the first-stage intervention options (A1), given whether or not the child received medication at school prior to first-stage intervention (O11).

Source: PubMed

Prossimi studi clinici

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