Literature DB >> 25485028

Comment on "Dynamic treatment regimes: technical challenges and applications"

Yair Goldberg1, Rui Song2, Donglin Zeng3, Michael R Kosorok3.   

Abstract

Inference for parameters associated with optimal dynamic treatment regimes is challenging as these estimators are nonregular when there are non-responders to treatments. In this discussion, we comment on three aspects of alleviating this nonregularity. We first discuss an alternative approach for smoothing the quality functions. We then discuss some further details on our existing work to identify non-responders through penalization. Third, we propose a clinically meaningful value assessment whose estimator does not suffer from nonregularity.

Entities:  

Year:  2014        PMID: 25485028      PMCID: PMC4255986          DOI: 10.1214/14-ejs905

Source DB:  PubMed          Journal:  Electron J Stat        ISSN: 1935-7524            Impact factor:   1.125


  1 in total

1.  Estimating Optimal Dynamic Regimes: Correcting Bias under the Null: [Optimal dynamic regimes: bias correction].

Authors:  Erica E M Moodie; Thomas S Richardson
Journal:  Scand Stat Theory Appl       Date:  2009-09-22       Impact factor: 1.396

  1 in total
  2 in total

1.  TARGETED SEQUENTIAL DESIGN FOR TARGETED LEARNING INFERENCE OF THE OPTIMAL TREATMENT RULE AND ITS MEAN REWARD.

Authors:  Antoine Chambaz; Wenjing Zheng; Mark J van der Laan
Journal:  Ann Stat       Date:  2017-12-15       Impact factor: 4.028

2.  Optimal Individualized Treatments in Resource-Limited Settings.

Authors:  Alexander R Luedtke; Mark J van der Laan
Journal:  Int J Biostat       Date:  2016-05-01       Impact factor: 0.968

  2 in total

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