Doubly Robust Estimation of Optimal Dynamic Treatment Regimes

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Doubly Robust Estimation of Optimal Dynamic Treatment Regimes. / Barrett, Jessica K; Henderson, Robin; Rosthøj, Susanne.

I: Statistics in BioSciences, Bind 6, Nr. 2, 2014, s. 244-260.

Publikation: Bidrag til tidsskriftTidsskriftartikelForskningfagfællebedømt

Harvard

Barrett, JK, Henderson, R & Rosthøj, S 2014, 'Doubly Robust Estimation of Optimal Dynamic Treatment Regimes', Statistics in BioSciences, bind 6, nr. 2, s. 244-260. https://doi.org/10.1007/s12561-013-9097-6

APA

Barrett, J. K., Henderson, R., & Rosthøj, S. (2014). Doubly Robust Estimation of Optimal Dynamic Treatment Regimes. Statistics in BioSciences, 6(2), 244-260. https://doi.org/10.1007/s12561-013-9097-6

Vancouver

Barrett JK, Henderson R, Rosthøj S. Doubly Robust Estimation of Optimal Dynamic Treatment Regimes. Statistics in BioSciences. 2014;6(2):244-260. https://doi.org/10.1007/s12561-013-9097-6

Author

Barrett, Jessica K ; Henderson, Robin ; Rosthøj, Susanne. / Doubly Robust Estimation of Optimal Dynamic Treatment Regimes. I: Statistics in BioSciences. 2014 ; Bind 6, Nr. 2. s. 244-260.

Bibtex

@article{9044287fcfb040dc9404dcd5d2087bd2,
title = "Doubly Robust Estimation of Optimal Dynamic Treatment Regimes",
abstract = "We compare methods for estimating optimal dynamic decision rules from observational data, with particular focus on estimating the regret functions defined by Murphy (in J. R. Stat. Soc., Ser. B, Stat. Methodol. 65:331-355, 2003). We formulate a doubly robust version of the regret-regression approach of Almirall et al. (in Biometrics 66:131-139, 2010) and Henderson et al. (in Biometrics 66:1192-1201, 2010) and demonstrate that it is equivalent to a reduced form of Robins' efficient g-estimation procedure (Robins, in Proceedings of the Second Symposium on Biostatistics. Springer, New York, pp. 189-326, 2004). Simulation studies suggest that while the regret-regression approach is most efficient when there is no model misspecification, in the presence of misspecification the efficient g-estimation procedure is more robust. The g-estimation method can be difficult to apply in complex circumstances, however. We illustrate the ideas and methods through an application on control of blood clotting time for patients on long term anticoagulation.",
author = "Barrett, {Jessica K} and Robin Henderson and Susanne Rosth{\o}j",
year = "2014",
doi = "10.1007/s12561-013-9097-6",
language = "English",
volume = "6",
pages = "244--260",
journal = "Statistics in Biosciences",
issn = "1867-1764",
publisher = "Springer",
number = "2",

}

RIS

TY - JOUR

T1 - Doubly Robust Estimation of Optimal Dynamic Treatment Regimes

AU - Barrett, Jessica K

AU - Henderson, Robin

AU - Rosthøj, Susanne

PY - 2014

Y1 - 2014

N2 - We compare methods for estimating optimal dynamic decision rules from observational data, with particular focus on estimating the regret functions defined by Murphy (in J. R. Stat. Soc., Ser. B, Stat. Methodol. 65:331-355, 2003). We formulate a doubly robust version of the regret-regression approach of Almirall et al. (in Biometrics 66:131-139, 2010) and Henderson et al. (in Biometrics 66:1192-1201, 2010) and demonstrate that it is equivalent to a reduced form of Robins' efficient g-estimation procedure (Robins, in Proceedings of the Second Symposium on Biostatistics. Springer, New York, pp. 189-326, 2004). Simulation studies suggest that while the regret-regression approach is most efficient when there is no model misspecification, in the presence of misspecification the efficient g-estimation procedure is more robust. The g-estimation method can be difficult to apply in complex circumstances, however. We illustrate the ideas and methods through an application on control of blood clotting time for patients on long term anticoagulation.

AB - We compare methods for estimating optimal dynamic decision rules from observational data, with particular focus on estimating the regret functions defined by Murphy (in J. R. Stat. Soc., Ser. B, Stat. Methodol. 65:331-355, 2003). We formulate a doubly robust version of the regret-regression approach of Almirall et al. (in Biometrics 66:131-139, 2010) and Henderson et al. (in Biometrics 66:1192-1201, 2010) and demonstrate that it is equivalent to a reduced form of Robins' efficient g-estimation procedure (Robins, in Proceedings of the Second Symposium on Biostatistics. Springer, New York, pp. 189-326, 2004). Simulation studies suggest that while the regret-regression approach is most efficient when there is no model misspecification, in the presence of misspecification the efficient g-estimation procedure is more robust. The g-estimation method can be difficult to apply in complex circumstances, however. We illustrate the ideas and methods through an application on control of blood clotting time for patients on long term anticoagulation.

U2 - 10.1007/s12561-013-9097-6

DO - 10.1007/s12561-013-9097-6

M3 - Journal article

C2 - 25484995

VL - 6

SP - 244

EP - 260

JO - Statistics in Biosciences

JF - Statistics in Biosciences

SN - 1867-1764

IS - 2

ER -

ID: 161677274