Sequential Multiple Assignment Randomized Trial (SMART) with Adaptive Randomization for Quality Improvement in Depression Treatment Program
Open accessAbstract
Summary: Implementation study is an important tool for deploying state-of-the-art treatments from clinical efficacy studies into a treatment program, with the dual goals of learning about effectiveness of the treatments and improving the quality of care for patients enrolled into the program. In this article, we deal with the design of a treatment program of dynamic treatment regimens (DTRs) for patients with depression post-acute coronary syndrome. We introduce a novel adaptive randomization scheme for a sequential multiple assignment randomized trial of DTRs. Our approach adapts the randomization probabilities to favor treatment sequences having comparatively superior Q-functions used in Q-learning. The proposed approach addresses three main concerns of an implementation study: it allows incorporation of historical data or opinions, it includes randomization for learning purposes, and it aims to improve care via adaptation throughout the program. We demonstrate how to apply our method to design a depression treatment program using data from a previous study. By simulation, we illustrate that the inputs from historical data are important for the program performance measured by the expected outcomes of the enrollees, but also show that the adaptive randomization scheme is able to compensate poorly specified historical inputs by improving patient outcomes within a reasonable horizon. The simulation results also confirm that the proposed design allows efficient learning of the treatments by alleviating the curse of dimensionality.
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Multiphase Optimization Strategy Implementation Trials (MOST) can be used to identify and test an optimized implementation intervention when testing all combinations in a single design is not feasible.
“Testing all combinations in a single design is not feasible, but MOST can be used to identify and test an optimized intervention.”
From An Overview of Research and Evaluation Designs for Dissemination and Implementation (2017) · cites this paper as [29]
MOST has three phases: preparation, optimization, and evaluation.
“MOST has three phases.”
From An Overview of Research and Evaluation Designs for Dissemination and Implementation (2017) · cites this paper as [29]
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Cites
Statistical Power Analysis for the Behavioral Sciences
Muller, K. E., & Cohen, J. (1989) · Technometrics · doi:10.2307/1270020 · Cited by 99,588 · see citing works
Estimating causal effects of treatments in randomized and nonrandomized studies
Rubin, D. B. (1974) · Journal of Educational Psychology · doi:10.1037/h0037350 · Cited by 9,436 · see citing works
Watkins, C. (1989) · OpenGrey (Institut de l'Information Scientifique et Technique) · doi:10.17863/cam.132164 · Cited by 5,406 · see citing works
Sequenced treatment alternatives to relieve depression (STAR*D): Rationale and design
Rush, A. J., Fava, M., Wisniewski, S. R., Lavori, P. W., Trivedi, M. H., Sackeim, H. A., Thase, M. E., Nierenberg, A. A., Quitkin, F. M., Kashner, T. M., et al. (2004) · Controlled Clinical Trials · doi:10.1016/s0197-2456(03)00112-0 · Cited by 1,076 · see citing works
Optimal Dynamic Treatment Regimes
Murphy, S. A. (2003) · Journal of the Royal Statistical Society Series B (Statistical Methodology) · doi:10.1111/1467-9868.00389 · Cited by 988 · see citing works
I-SPY 2: An Adaptive Breast Cancer Trial Design in the Setting of Neoadjuvant Chemotherapy
Barker, A., Sigman, C. C., Gj, K., Hylton, N., Berry, D., & Esserman, L. J. (2009) · Clinical Pharmacology & Therapeutics · doi:10.1038/clpt.2009.68 · Cited by 767 · see citing works
An experimental design for the development of adaptive treatment strategies
Murphy, S. A. (2005) · Statistics in Medicine · doi:10.1002/sim.2022 · Cited by 595 · see citing works
Causal Inference from Complex Longitudinal Data
Robins, J. M. (1997) · Lecture Notes in Statistics · doi:10.1007/978-1-4612-1842-5_4 · Cited by 510 · see citing works
Depression Screening and Patient Outcomes in Cardiovascular Care: A Systematic Review
Thombs, B. D., de Jonge, P., Coyne, J. C., Whooley, M. A., Frasure-Smith, N., Mitchell, A. J., Zuidersma, M., Eze-Nliam, C., Lima, B. B., Smith, C. G., et al. (2008) · JAMA · doi:10.1001/jama.2008.667 · Cited by 472 · see citing works
Practical Bayesian adaptive randomisation in clinical trials
Thall, P. F., & Wathen, J. K. (2007) · European Journal of Cancer · doi:10.1016/j.ejca.2007.01.006 · Cited by 230 · see citing works
Dynamic treatment regimes: practical design considerations
Lavori, P. W., & Dawson, R. (2004) · Clinical Trials · doi:10.1191/1740774s04cn002oa · Cited by 229 · see citing works
Model calibration in the continual reassessment method
Lee, S. M., & Cheung, Y. K. (2009) · Clinical Trials · doi:10.1177/1740774509105076 · Cited by 225 · see citing works
Self-citationA Generalization Error for Q-Learning.
Murphy, S. A. (2005) · PubMed · Cited by 216 · see citing works
Schneider, L. S. S., Tariot, P. N., Lyketsos, C. G., Dagerman, K., Davis, K. L., Thomas, S., Hsiao, J., Jeste, D. V., Katz, I. R., Olin, J. T., et al. (2001) · American Journal of Geriatric Psychiatry · doi:10.1097/00019442-200111000-00004 · Cited by 211 · see citing works
Davidson, K. W., Bigger, J. T., Burg, M. M., Carney, R. M., Chaplin, W. F., Czajkowski, S., Dornelas, E., Duer-Hefele, J., Frasure-Smith, N., Freedland, K. E., et al. (2013) · JAMA Internal Medicine · doi:10.1001/jamainternmed.2013.915 · Cited by 162 · see citing works
Self-citationInference for Optimal Dynamic Treatment Regimes Using an Adaptive m‐Out‐of‐n Bootstrap Scheme
Chakraborty, B., Laber, E. B., & Zhao, Y. (2013) · Biometrics · doi:10.1111/biom.12052 · Cited by 113 · see citing works
Self-citationQ-Learning: Flexible Learning About Useful Utilities
Moodie, E. E. M., Dean, N., & Sun, Y. (2013) · Statistics in Biosciences · doi:10.1007/s12561-013-9103-z · Cited by 112 · see citing works
Strategies for comparing treatments on a binary response with multi-centre data
Agresti, A., & Hartzel, J. S. (2000) · Statistics in Medicine · doi:10.1002/(sici)1097-0258(20000430)19:8 · Cited by 102 · see citing works
Thall, P. F., Wooten, L. H., Logothetis, C. J., Millikan, R. E., & Tannir, N. M. (2007) · Statistics in Medicine · doi:10.1002/sim.2894 · Cited by 75 · see citing works
Calibration of prior variance in the Bayesian continual reassessment method
Lee, S. M., & Cheung, Y. K. (2011) · Statistics in Medicine · doi:10.1002/sim.4139 · Cited by 66 · see citing works
Self-citationContinuous Bayesian adaptive randomization based on event times with covariates
Cheung, Y. K., Inoue, L. Y. T., Wathen, J. K., & Thall, P. F. (2005) · Statistics in Medicine · doi:10.1002/sim.2247 · Cited by 57 · see citing works
Self-citationBayesian Dose-Finding in Two Treatment Cycles Based on the Joint Utility of Efficacy and Toxicity
Lee, J., Thall, P. F., Ji, Y., & Müller, P. (2014) · Journal of the American Statistical Association · doi:10.1080/01621459.2014.926815 · Cited by 52 · see citing works
Cheung, K., & Duan, N. (2014) · American Journal of Public Health · doi:10.2105/ajph.2013.301579 · Cited by 26 · see citing works
Cheung, Y. K. (2007) · Biometrics · doi:10.1111/j.1541-0420.2007.00929.x · Cited by 14 · see citing works
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An Overview of Research and Evaluation Designs for Dissemination and Implementation
Brown, C. H., Curran, G., Palinkas, L. A., Aarons, G. A., Wells, K. B., Jones, L., Collins, L. M., Duan, N., Mittman, B. S., Wallace, A., et al. (2017) · Annual Review of Public Health · doi:10.1146/annurev-publhealth-031816-044215 · Cited by 509 · see citing works
Experimental and quasi-experimental designs in implementation research
Miller, C. J., Smith, S. N., & Pugatch, M. (2020) · Psychiatry Research · doi:10.1016/j.psychres.2019.06.027 · Cited by 373 · see citing works
“Adaptive designs could potentially be incorporated into both SMART designs and stepped wedge studies, as well as traditional RCTs to further advance implementation science (Cheung et al., 2015).”
Method
- Authors
- Cheung, Y. K., Chakraborty, B., & Davidson, K. W.
- Affiliations
- Columbia University (US)
- National University of Singapore (SG)
- Columbia University Irving Medical Center (US)
- Corresponding author
- Cheung, Ying Kuen
- Venue
- Biometrics
- Publisher
- Oxford University Press (OUP)
- Published
- 2014-10-29
- Volume
- 71
- Issue
- 2
- Pages
- 450-459
- ISSN
- 0006-341X (ISSN-L)
- 1541-0420
- PMC
- PMC4429017
- OpenAlex
- W1508428170
- Cited by
- 55
- References
- 24
- Citations by year
- 2026: 4 · 2025: 1 · 2024: 6 · 2023: 3 · 2022: 6 · 2021: 10 · 2020: 5 · 2019: 3 · 2018: 6 · 2017: 6 · 2016: 3 · 2015: 2
- FWCI
- 3.87
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Licence
- academic.oup.com/journals/pages/open_access/funder_policies/chorus/standard_publication_model (vor)
- Locations
- Biometrics (publishedVersion)
- PubMed (publishedVersion)
- Europe PMC (PubMed Central) (acceptedVersion)
- PubMed Central (acceptedVersion)
- RePEc: Research Papers in Economics (submittedVersion)
- Funders
- NIH (R01 NS072127)
- Topics
- Physical Sciences › Mathematics › Statistics and Probability › Advanced Causal Inference Techniques
- Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods in Clinical Trials
- Social Sciences › Decision Sciences › Management Science and Operations Research › Simulation Techniques and Applications
- Keywords
- sequential multiple assignment randomized trial, adaptive randomization, dynamic treatment regimens, depression treatment, Q-learning, implementation research, quality improvement, curse of dimensionality, treatment program design
- MeSH terms
- Adaptation, Psychological, Behavior Therapy, Biometry, Computer Simulation, Depression, Humans, Models, Statistical, Randomized Controlled Trials as Topic, Acute Coronary Syndrome
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