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Zhao YQ, Laber EB. Estimation of optimal dynamic treatment regimes. Clin Trials. 2014;11(4):400-407doi: 10.1177/1740774514532570.
Zhao, Y. Q., & Laber, E. B. (2014). Estimation of optimal dynamic treatment regimes. Clinical trials (London, England), 11(4), 400-407. https://doi.org/10.1177/1740774514532570
Zhao, Ying-Qi, and Laber, Eric B. "Estimation of optimal dynamic treatment regimes." Clinical trials (London, England) vol. 11,4 (2014): 400-407. doi: https://doi.org/10.1177/1740774514532570
Zhao YQ, Laber EB. Estimation of optimal dynamic treatment regimes. Clin Trials. 2014 Aug;11(4):400-407. doi: 10.1177/1740774514532570. Epub 2014 May 28. PMID: 24872361; PMCID: PMC4247353.
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Clin Trials. 2014 Aug;11(4):400-407. doi: 10.1177/1740774514532570. Epub 2014 May 28.

Estimation of optimal dynamic treatment regimes.

Clinical trials (London, England)

Ying-Qi Zhao, Eric B Laber

Affiliations

  1. Department of Biostatistics and Medical Informatics, University of Wisconsin-Madison, Madison, WI, USA [email protected].
  2. Department of Statistics, North Carolina State University, Raleigh, NC, USA.

PMID: 24872361 PMCID: PMC4247353 DOI: 10.1177/1740774514532570

Abstract

BACKGROUND: Recent advances in medical research suggest that the optimal treatment rules should be adaptive to patients over time. This has led to an increasing interest in studying dynamic treatment regime, a sequence of individualized treatment rules, one per stage of clinical intervention, which maps present patient information to a recommended treatment. There has been a recent surge of statistical work for estimating optimal dynamic treatment regimes from randomized and observational studies. The purpose of this article is to review recent methodological progress and applied issues associated with estimating optimal dynamic treatment regimes.

METHODS: We discuss sequential multiple assignment randomized trials, a clinical trial design used to study treatment sequences. We use a common estimator of an optimal dynamic treatment regime that applies to sequential multiple assignment randomized trials data as a platform to discuss several practical and methodological issues.

RESULTS: We provide a limited survey of practical issues associated with modeling sequential multiple assignment randomized trials data. We review some existing estimators of optimal dynamic treatment regimes and discuss practical issues associated with these methods including model building, missing data, statistical inference, and choosing an outcome when only non-responders are re-randomized. We mainly focus on the estimation and inference of dynamic treatment regimes using sequential multiple assignment randomized trials data. Dynamic treatment regimes can also be constructed from observational data, which may be easier to obtain in practice; however, care must be taken to account for potential confounding.

© The Author(s), 2014.

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