Document Type
Article
Publication Date
7-8-2024
Abstract
We present a trial design for sequential multiple assignment randomized trials (SMARTs) that use a tailoring function instead of a binary tailoring variable allowing for simultaneous development of the tailoring variable and estimation of dynamic treatment regimens (DTRs). We apply methods for developing DTRs from observational data: tree-based regression learning and Q-learning. We compare this to a balanced randomized SMART with equal re-randomization probabilities and a typical SMART design where re-randomization depends on a binary tailoring variable and DTRs are analyzed with weighted and replicated regression. This project addresses a gap in clinical trial methodology by presenting SMARTs where second stage treatment is based on a continuous outcome removing the need for a binary tailoring variable. We demonstrate that data from a SMART using a tailoring function can be used to efficiently estimate DTRs and is more flexible under varying scenarios than a SMART using a tailoring variable.
Keywords
clinical trials, dynamic treatment regimens, Q-learning, SMARTs, tailoring function, tailoring variable, tree based reinforcement learning
Publication Title
Statistics in Medicine
Grant
P30‐CA046592
Rights
© 2024 The Author(s). Statistics in Medicine published by John Wiley & Sons Ltd. This is an open access article under the terms of the Creative Commons Attribution-NonCommercial-NoDerivs License, which permits use and distribution in any medium, provided the original work is properly cited, the use is non-commercial and no modifications or adaptations are made.
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.
Recommended Citation
Hartman H, Schipper M, Kidwell K. A sequential, multiple assignment, randomized trial design with a tailoring function. Statistics in Medicine. 2024; 43(21): 4055-4072. doi: 10.1002/sim.10161
Manuscript Version
Final Publisher Version