Open Access
2017 Tree based weighted learning for estimating individualized treatment rules with censored data
Yifan Cui, Ruoqing Zhu, Michael Kosorok
Electron. J. Statist. 11(2): 3927-3953 (2017). DOI: 10.1214/17-EJS1305


Estimating individualized treatment rules is a central task for personalized medicine. [25] and [23] proposed outcome weighted learning to estimate individualized treatment rules directly through maximizing the expected outcome without modeling the response directly. In this paper, we extend the 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 [26]. To accomplish this, we take advantage of the tree based approach proposed in [29] 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 in the second approach only the censored observations are imputed by their conditional expected failure times. 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.


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Yifan Cui. Ruoqing Zhu. Michael Kosorok. "Tree based weighted learning for estimating individualized treatment rules with censored data." Electron. J. Statist. 11 (2) 3927 - 3953, 2017.


Received: 1 October 2016; Published: 2017
First available in Project Euclid: 18 October 2017

zbMATH: 1379.62066
MathSciNet: MR3714303
Digital Object Identifier: 10.1214/17-EJS1305

Keywords: consistency , Individualized treatment rule , nonparametric estimation , outcome weighted learning , recursively imputed survival trees , Right censored data

Vol.11 • No. 2 • 2017
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