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On sparse representation for optimal individualized treatment selection with penalized outcome weighted learning
Author(s) -
Song Rui,
Kosorok Michael,
Zeng Donglin,
Zhao Yingqi,
Laber Eric,
Yuan Ming
Publication year - 2015
Publication title -
stat
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.61
H-Index - 18
ISSN - 2049-1573
DOI - 10.1002/sta4.78
Subject(s) - feature selection , outcome (game theory) , personalized medicine , estimator , consistency (knowledge bases) , selection (genetic algorithm) , computer science , variable (mathematics) , artificial intelligence , machine learning , lasso (programming language) , mathematics , statistics , bioinformatics , mathematical analysis , mathematical economics , world wide web , biology
As a new strategy for treatment, which takes individual heterogeneity into consideration, personalized medicine is of growing interest. Discovering individualized treatment rules for patients who have heterogeneous responses to treatment is one of the important areas in developing personalized medicine. As more and more information per individual is being collected in clinical studies and not all of the information is relevant for treatment discovery, variable selection becomes increasingly important in discovering individualized treatment rules. In this article, we develop a variable selection method based on penalized outcome weighted learning through which an optimal treatment rule is considered as a classification problem where each subject is weighted proportional to his or her clinical outcome. We show that the resulting estimator of the treatment rule is consistent and establish variable selection consistency and the asymptotic distribution of the estimators. The performance of the proposed approach is demonstrated via simulation studies and an analysis of chronic depression data. Copyright © 2015 John Wiley & Sons, Ltd.