Literature DB >> 20002404

Regret-regression for optimal dynamic treatment regimes.

Robin Henderson1, Phil Ansell, Deyadeen Alshibani.   

Abstract

We consider optimal dynamic treatment regime determination in practice. Model building, checking, and comparison have had little or no attention so far in this literature. Motivated by an application on optimal dosage of anticoagulants, we propose a modeling and estimation strategy that incorporates the regret functions of Murphy (2003, Journal of the Royal Statistical Society, Series B 65, 331-366) into a regression model for observed responses. Estimation is quick and diagnostics are available, meaning a variety of candidate models can be compared. The method is illustrated using simulation and the anticoagulation application.
© 2009, The International Biometric Society.

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Year:  2010        PMID: 20002404     DOI: 10.1111/j.1541-0420.2009.01368.x

Source DB:  PubMed          Journal:  Biometrics        ISSN: 0006-341X            Impact factor:   2.571


  33 in total

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Journal:  Biometrics       Date:  2014-01-08       Impact factor: 2.571

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

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Journal:  Stat Sci       Date:  2014-11       Impact factor: 2.901

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6.  Greedy outcome weighted tree learning of optimal personalized treatment rules.

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Journal:  Biometrics       Date:  2016-10-04       Impact factor: 2.571

7.  Multi-Objective Markov Decision Processes for Data-Driven Decision Support.

Authors:  Daniel J Lizotte; Eric B Laber
Journal:  J Mach Learn Res       Date:  2016-12-01       Impact factor: 3.654

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Authors:  Michael R Kosorok; Eric B Laber
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9.  Q-learning for estimating optimal dynamic treatment rules from observational data.

Authors:  Erica E M Moodie; Bibhas Chakraborty; Michael S Kramer
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10.  Time-varying effect moderation using the structural nested mean model: estimation using inverse-weighted regression with residuals.

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Journal:  Stat Med       Date:  2013-07-19       Impact factor: 2.373

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