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Activity Number: 35 - Special Session: Section on Nonparametric Statistics Student Paper Competition
Type: Contributed
Date/Time: Sunday, July 30, 2017 : 2:00 PM to 3:50 PM
Sponsor: Section on Nonparametric Statistics
Abstract #322664
Title: Tree Based Weighted Learning for Estimating Individualized Treatment Rules with Censored Data
Author(s): Yifan Cui* and Ruoqing Zhu and Michael R Kosorok
Companies: University of North Carolina at Chapel Hill and University of Illinois Urbana-Champaign and University of North Carolina at Chapel Hill
Keywords: Individualized treatment rule ; Nonparametric estimation ; Right censored data ; Consistency ; Recursively imputed survival trees ; Outcome weighted learning

Estimating individualized treatment rules is a central task for personalized medicine. Zhao at al. (2012) and Zhang at al. (2012) proposed outcome weighted learning to estimate individualized treatment rules directly through maximizing the expected clinical response without modeling the response. In this paper, we extend outcome weighted learning to right censored survival data without requiring either inverse probability of censoring weighting or semiparametric modeling of the censoring and failure times as done in Zhao at al. (2015b). To accomplish this, we take advantage of the tree based approach proposed in Zhu and Kosorok (2012) to nonparametrically impute the survival time in two different ways. The first approach replaces the reward of each individual by the expected survival time, while the second method imputes the expected failure time conditional on the observed censoring time. We establish consistency and convergence rates for both estimators. In simulation studies, our estimators demonstrate improved performance compared to existing methods. We also illustrate the proposed method on a phase III clinical trial of non-small cell lung cancer.

Authors who are presenting talks have a * after their name.

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