Literature DB >> 28003710

Comment.

Jingxiang Chen1, Yufeng Liu2, Donglin Zeng1, Rui Song3, Yingqi Zhao4, Michael R Kosorok5.   

Abstract

Xu, Müller, Wahed, and Thall proposed a Bayesian model to analyze an acute leukemia study involving multi-stage chemotherapy regimes. We discuss two alternative methods, Q-learning and O-learning, to solve the same problem from the machine learning point of view. The numerical studies show that these methods can be flexible and have advantages in some situations to handle treatment heterogeneity while being robust to model misspecification.

Entities:  

Keywords:  Dynamic treatment regimes; Multi-stage chemotherapy regimes; O-learning; Q-learning

Year:  2016        PMID: 28003710      PMCID: PMC5167482          DOI: 10.1080/01621459.2016.1200914

Source DB:  PubMed          Journal:  J Am Stat Assoc        ISSN: 0162-1459            Impact factor:   5.033


  8 in total

1.  Subgroup analyses in randomized trials: risks of subgroup-specific analyses; power and sample size for the interaction test.

Authors:  Sara T Brookes; Elise Whitely; Matthias Egger; George Davey Smith; Paul A Mulheran; Tim J Peters
Journal:  J Clin Epidemiol       Date:  2004-03       Impact factor: 6.437

2.  Treating individuals 2. Subgroup analysis in randomised controlled trials: importance, indications, and interpretation.

Authors:  Peter M Rothwell
Journal:  Lancet       Date:  2005 Jan 8-14       Impact factor: 79.321

3.  Doubly Robust Learning for Estimating Individualized Treatment with Censored Data.

Authors:  Y Q Zhao; D Zeng; E B Laber; R Song; M Yuan; M R Kosorok
Journal:  Biometrika       Date:  2015-03-01       Impact factor: 2.445

4.  Optimal treatment regimes for survival endpoints using a locally-efficient doubly-robust estimator from a classification perspective.

Authors:  Xiaofei Bai; Anastasios A Tsiatis; Wenbin Lu; Rui Song
Journal:  Lifetime Data Anal       Date:  2016-08-01       Impact factor: 1.588

5.  Analysis and interpretation of treatment effects in subgroups of patients in randomized clinical trials.

Authors:  S Yusuf; J Wittes; J Probstfield; H A Tyroler
Journal:  JAMA       Date:  1991-07-03       Impact factor: 56.272

6.  Q-LEARNING WITH CENSORED DATA.

Authors:  Yair Goldberg; Michael R Kosorok
Journal:  Ann Stat       Date:  2012-02-01       Impact factor: 4.028

7.  New Statistical Learning Methods for Estimating Optimal Dynamic Treatment Regimes.

Authors:  Ying-Qi Zhao; Donglin Zeng; Eric B Laber; Michael R Kosorok
Journal:  J Am Stat Assoc       Date:  2015       Impact factor: 5.033

8.  Robust estimation of optimal dynamic treatment regimes for sequential treatment decisions.

Authors:  Baqun Zhang; Anastasios A Tsiatis; Eric B Laber; Marie Davidian
Journal:  Biometrika       Date:  2013       Impact factor: 2.445

  8 in total

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