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Schulte PJ, Tsiatis AA, Laber EB, et al. Q- and A-learning Methods for Estimating Optimal Dynamic Treatment Regimes. Stat Sci. 2014;29(4):640-661doi: 10.1214/13-STS450.
Schulte, P. J., Tsiatis, A. A., Laber, E. B., & Davidian, M. (2014). Q- and A-learning Methods for Estimating Optimal Dynamic Treatment Regimes. Statistical science : a review journal of the Institute of Mathematical Statistics, 29(4), 640-661. https://doi.org/10.1214/13-STS450
Schulte, Phillip J, et al. "Q- and A-learning Methods for Estimating Optimal Dynamic Treatment Regimes." Statistical science : a review journal of the Institute of Mathematical Statistics vol. 29,4 (2014): 640-661. doi: https://doi.org/10.1214/13-STS450
Schulte PJ, Tsiatis AA, Laber EB, Davidian M. Q- and A-learning Methods for Estimating Optimal Dynamic Treatment Regimes. Stat Sci. 2014 Nov;29(4):640-661. doi: 10.1214/13-STS450. PMID: 25620840; PMCID: PMC4300556.
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Stat Sci. 2014 Nov;29(4):640-661. doi: 10.1214/13-STS450.

Q- and A-learning Methods for Estimating Optimal Dynamic Treatment Regimes.

Statistical science : a review journal of the Institute of Mathematical Statistics

Phillip J Schulte, Anastasios A Tsiatis, Eric B Laber, Marie Davidian

Affiliations

  1. Biostatistician, Duke Clinical Research Institute, Durham, North Carolina 27701, USA ( [email protected] ).
  2. Gertrude M. Cox Distinguished Professor, Department of Statistics, North Carolina State University, Raleigh, North Carolina 27695-8203, USA ( [email protected] ).
  3. Assistant Professor, Department of Statistics, North Carolina State University, Raleigh, North Carolina 27695-8203, USA ( [email protected] ).
  4. William Neal Reynolds Professor, Department of Statistics, North Carolina State University, Raleigh, North Carolina 27695-8203, USA ( [email protected] ).

PMID: 25620840 PMCID: PMC4300556 DOI: 10.1214/13-STS450

Abstract

In clinical practice, physicians make a series of treatment decisions over the course of a patient's disease based on his/her baseline and evolving characteristics. A dynamic treatment regime is a set of sequential decision rules that operationalizes this process. Each rule corresponds to a decision point and dictates the next treatment action based on the accrued information. Using existing data, a key goal is estimating the optimal regime, that, if followed by the patient population, would yield the most favorable outcome on average.

Keywords: Advantage learning; bias-variance tradeoff; model misspecification; personalized medicine; potential outcomes; sequential decision making

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