|On Sparse representation for Optimal Individualized Treatment Selection with Penalized Outcome Weighted Learning.
|Year of Publication
|Song, Rui, Michael Kosorok, Donglin Zeng, Yingqi Zhao, Eric Laber, and Ming Yuan
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.
|On sparse representation for optimal individualized treatment selection with penalized outcome weighted learning.
|PubMed Central ID
|P01 CA142538 / CA / NCI NIH HHS / United States
U01 NS082062 / NS / NINDS NIH HHS / United States