Q-Learning with Clustered-SMART (cSMART) Data: Examining Moderators in the Construction of Clustered Adaptive Interventions

• URL: http://arxiv.org/abs/2505.00822v1
• Date: Thu, 01 May 2025 19:24:39 GMT
• Title: Q-Learning with Clustered-SMART (cSMART) Data: Examining Moderators in the Construction of Clustered Adaptive Interventions
• Authors: Yao Song, Kelly Speth, Amy Kilbourne, Andrew Quanbeck, Daniel Almirall, Lu Wang,
• Abstract summary: A clustered adaptive intervention (cAI) is a sequence of decision rules that guides practitioners on how best to tailor cluster-level intervention to improve outcomes.<n>We introduce a clustered Q-learning framework with the M-out-of-N Cluster Bootstrap to evaluate whether a set of candidate tailoring variables may be useful in defining an optimal cAI.
• Score: 3.9650359172757743
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: A clustered adaptive intervention (cAI) is a pre-specified sequence of decision rules that guides practitioners on how best - and based on which measures - to tailor cluster-level intervention to improve outcomes at the level of individuals within the clusters. A clustered sequential multiple assignment randomized trial (cSMART) is a type of trial that is used to inform the empirical development of a cAI. The most common type of secondary aim in a cSMART focuses on assessing causal effect moderation by candidate tailoring variables. We introduce a clustered Q-learning framework with the M-out-of-N Cluster Bootstrap using data from a cSMART to evaluate whether a set of candidate tailoring variables may be useful in defining an optimal cAI. This approach could construct confidence intervals (CI) with near-nominal coverage to assess parameters indexing the causal effect moderation function. Specifically, it allows reliable inferences concerning the utility of candidate tailoring variables in constructing a cAI that maximizes a mean end-of-study outcome even when "non-regularity", a well-known challenge exists. Simulations demonstrate the numerical performance of the proposed method across varying non-regularity conditions and investigate the impact of varying number of clusters and intra-cluster correlation coefficient on CI coverage. Methods are applied on ADEPT dataset to inform the construction of a clinic-level cAI for improving evidence-based practice in treating mood disorders.

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