title: On Sparse representation for Optimal Individualized Treatment Selection with Penalized Outcome Weighted Learning.
abstract: As a new strategy for treatment which takes individual heterogeneity into consideration, personalized medicine is of growing interest. Discovering individualized treatment rules (ITRs) 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.
authors.affiliation: Department of Statistics, North Carolina State University, Raleigh, NC 27695.
authors.forename: Rui
authors.initials: R
authors.lastname: Song
authors.affiliation: Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, 27599.
authors.forename: Michael
authors.initials: M
authors.lastname: Kosorok
authors.affiliation: Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, 27599.
authors.forename: Donglin
authors.initials: D
authors.lastname: Zeng
authors.affiliation: Department of Statistics, University of Wisconsin-Madison, Madison, Wisconsin, 53792.
authors.forename: Yingqi
authors.initials: Y
authors.lastname: Zhao
authors.affiliation: Department of Statistics, North Carolina State University, Raleigh, NC 27695.
authors.forename: Eric
authors.initials: E
authors.lastname: Laber
authors.affiliation: Department of Statistics, University of Wisconsin-Madison, Madison, Wisconsin, 53792.
authors.forename: Ming
authors.initials: M
authors.lastname: Yuan
keywords: Penalization; Personalized medicine; Support vector machine