Literature DB >> 28757669

Comment.

Min Qian1.   

Abstract

This comment deals with issues related to the article by Chen, Zeng, and Kosorok. We present several potential modifications of the outcome weighted learning approach. Those modifications are based on truncated l2 loss. One advantage of l2 loss is that it is differentiable everywhere, which makes it more stable and computationally more tractable.

Entities:  

Keywords:  Double robustness; Epanechnikov kernel; Personalized treatment

Year:  2017        PMID： 28757669      PMCID： PMC5531078          DOI： 10.1080/01621459.2016.1243479

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


  5 in total

1.  Variable selection for optimal treatment decision.

Authors:  Wenbin Lu; Hao Helen Zhang; Donglin Zeng
Journal:  Stat Methods Med Res       Date:  2011-11-23       Impact factor: 3.021

2.  PERFORMANCE GUARANTEES FOR INDIVIDUALIZED TREATMENT RULES.

Authors:  Min Qian; Susan A Murphy
Journal:  Ann Stat       Date:  2011-04-01       Impact factor: 4.028

3.  A Boosting Algorithm for Estimating Generalized Propensity Scores with Continuous Treatments.

Authors:  Yeying Zhu; Donna L Coffman; Debashis Ghosh
Journal:  J Causal Inference       Date:  2014-08-01

4.  A robust method for estimating optimal treatment regimes.

Authors:  Baqun Zhang; Anastasios A Tsiatis; Eric B Laber; Marie Davidian
Journal:  Biometrics       Date:  2012-05-02       Impact factor: 2.571

5.  Estimating Individualized Treatment Rules Using Outcome Weighted Learning.

Authors:  Yingqi Zhao; Donglin Zeng; A John Rush; Michael R Kosorok
Journal:  J Am Stat Assoc       Date:  2012-09-01       Impact factor: 5.033
  5 in total

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