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Q-Learning with clustered-SMART (cSMART) data: examining moderators in the construction of clustered adaptive
Yao Song1, Kelly Speth1, Amy Kilbourne2,3
1Department of Biostatistics, School of Public Health, University of Michigan, Ann Arbor, MI 48109-2029, United States.
Biometrics
|May 21, 2026
Summary
This study introduces a clustered Q-learning framework to develop optimal clustered adaptive interventions (cAI) using clustered sequential multiple assignment randomized trials (cSMART). The method reliably identifies tailoring variables to improve patient outcomes, even with complex data challenges.
Area of Science:
- Statistics
- Public Health
- Machine Learning
Background:
- Clustered adaptive interventions (cAI) tailor treatments at the cluster level to improve individual outcomes.
- Clustered sequential multiple assignment randomized trials (cSMART) are crucial for developing cAIs and assessing causal effect moderation.
- Developing optimal cAIs requires robust methods to handle complex data and identify effective tailoring variables.
Purpose of the Study:
- To introduce a clustered Q-learning framework with M-out-of-N cluster bootstrap for evaluating candidate tailoring variables in cAI development.
- To assess the utility of candidate tailoring variables for constructing optimal cAIs that maximize end-of-study outcomes.
- To enable reliable inferences on causal effect moderation, even under challenging non-regularity conditions.
Main Methods:
- Utilized a clustered Q-learning framework combined with M-out-of-N cluster bootstrap.
- Applied the method to data from a cSMART to evaluate tailoring variables for cAI construction.
- Conducted simulations to assess performance across varying non-regularity, cluster numbers, and intra-cluster correlation coefficients.
Main Results:
- The proposed framework constructs confidence intervals with near-nominal coverage for causal effect moderation parameters.
- Demonstrated reliable inference for tailoring variable utility in cAI development, addressing non-regularity challenges.
- Simulations confirmed the method's numerical performance and the impact of design parameters on confidence interval coverage.
Conclusions:
- The clustered Q-learning approach provides a statistically sound method for developing optimal cAIs from cSMART data.
- This framework facilitates the identification of effective tailoring variables, enhancing evidence-based practice.
- The method was successfully applied to the ADEPT dataset to inform clinic-level cAI for mood disorder treatment.
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