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

Publication ,  Journal Article
Schulte, PJ; Tsiatis, AA; Laber, EB; Davidian, M
Published in: Stat Sci
November 2014

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. Q- and A-learning are two main approaches for this purpose. We provide a detailed account of these methods, study their performance, and illustrate them using data from a depression study.

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Published In

Stat Sci

DOI

ISSN

0883-4237

Publication Date

November 2014

Volume

29

Issue

4

Start / End Page

640 / 661

Location

United States

Related Subject Headings

  • Statistics & Probability
  • 4905 Statistics
  • 0104 Statistics
 

Citation

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ICMJE
MLA
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Schulte, P. J., Tsiatis, A. A., Laber, E. B., & Davidian, M. (2014). Q- and A-learning Methods for Estimating Optimal Dynamic Treatment Regimes. Stat Sci, 29(4), 640–661. https://doi.org/10.1214/13-STS450
Schulte, Phillip J., Anastasios A. Tsiatis, Eric B. Laber, and Marie Davidian. “Q- and A-learning Methods for Estimating Optimal Dynamic Treatment Regimes.Stat Sci 29, no. 4 (November 2014): 640–61. 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):640–61.
Schulte, Phillip J., et al. “Q- and A-learning Methods for Estimating Optimal Dynamic Treatment Regimes.Stat Sci, vol. 29, no. 4, Nov. 2014, pp. 640–61. Pubmed, doi: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.

Published In

Stat Sci

DOI

ISSN

0883-4237

Publication Date

November 2014

Volume

29

Issue

4

Start / End Page

640 / 661

Location

United States

Related Subject Headings

  • Statistics & Probability
  • 4905 Statistics
  • 0104 Statistics