Q-Learning: A Data Analysis Method for Constructing Adaptive Interventions
Q-Learning: A Data Analysis Method for Constructing Adaptive Interventions
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DOI：
10.1037/a0029373
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发表时间：
2012-12-01
影响因子：
7
通讯作者：
Murphy, Susan A.
中科院分区：
文献类型：
--
作者：
Nahum-Shani, Inbal;Qian, Min;Murphy, Susan A.
随着时间的推移，对个性化和适应性干预服务的兴趣日益增加，导致了适应性干预的发展。适应性干预是通过使用输入参与者信息和输出干预建议的决策规则，随着时间的推移，将一系列干预方案个性化。我们介绍q学习，这是回归分析的一种概括，在这种情况下，有关干预选择或服务的一系列决策被做出。使用Q表示该方法用于评估干预方案的相对合格性。特别是，我们使用Q-learning和线性回归来估计最优（即最有效）的决策规则序列。我们说明了q团队如何与顺序多任务随机试验（SMARTs; Murphy, 2005）的数据一起使用，以告知构建比SMART设计中嵌入的更深入定制的决策规则序列。我们还讨论了与其他数据分析方法相比，Q-learning的优势。最后，我们使用ADHD儿童SMART研究的适应性干预（纽约州立大学布法罗大学儿童和家庭中心，William E. Pelham担任首席研究员）来说明。
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.