A sequential, multiple assignment, randomized trial design with a tailoring function

DOI10.1002/sim.10161MaRDI QIDQ6656300

Holly Hartman, M. Schipper, Kelley M. Kidwell

Publication date: 2 January 2025

Published in: Statistics in Medicine (Search for Journal in Brave)

zbMATH Keywords

clinical trialsQ-learningdynamic treatment regimensSMARTstailoring functiontailoring variabletree based reinforcement learning

Mathematics Subject Classification ID

Applications of statistics to biology and medical sciences; meta analysis (62P10)

Cites Work

• Longitudinal data analysis using generalized linear models
• Tree-based reinforcement learning for estimating optimal dynamic treatment regimes
• Q-learning for estimating optimal dynamic treatment rules from observational data
• Tree-based methods for individualized treatment regimes
• The central role of the propensity score in observational studies for causal effects
• Marginal Mean Models for Dynamic Regimes
• A Bayesian Machine Learning Approach for Optimizing Dynamic Treatment Regimes
• Estimating Individualized Treatment Rules Using Outcome Weighted Learning
• Design and analysis considerations for comparing dynamic treatment regimens with binary outcomes from sequential multiple assignment randomized trials
• Design and analysis considerations for utilizing a mapping function in a small sample, sequential, multiple assignment, randomized trials with continuous outcomes

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