Outcome weighted ψ-learning for individualized treatment rules

Mingyang Liu, Xiaotong Shen, Wei Pan

Research output: Contribution to journalArticlepeer-review

Abstract

An individualized treatment rule is often employed to maximize a certain patient-specific clinical outcome based on his/her clinical or genomic characteristics as well as heterogeneous response to treatments. Although developing such a rule is conceptually important to personalized medicine, existing methods such as the partial least squares suffers from the difficulty of indirect maximization of a patient's clinical outcome, while the outcome weighted learning is not robust against any perturbation of the outcome. In this article, we propose a weighted ψ-learning method to optimize an individualized treatment rule, which is robust against any data perturbation near the decision boundary by seeking the maximum separation. To solve non-convex minimization, we employ a difference convex algorithm to relax the non-convex minimization iteratively based on a decomposition of the cost function into a difference of two convex functions. On this ground, we also introduce a variable selection method for further removing redundant variables for a higher performance. Finally, we illustrate the proposed method by simulations and a lung health study, and we demonstrate that it yields higher performances in terms of accuracy of prediction of individualized treatment.

Original languageEnglish (US)
Article numbere343
JournalStat
Volume10
Issue number1
DOIs
StatePublished - Dec 2021

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© 2020 John Wiley & Sons, Ltd.

PubMed: MeSH publication types

  • Journal Article

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