Tree based weighted learning for estimating individualized treatment rules with censored data

TL;DR

This paper extends outcome weighted learning to censored survival data using tree-based imputation methods, enabling personalized treatment rule estimation without complex modeling or weighting schemes.

Contribution

It introduces a novel tree-based approach for nonparametric imputation of survival times in outcome weighted learning, applicable to censored data, with proven consistency and improved performance.

Findings

Improved estimation accuracy over existing methods.

Consistent estimators with established convergence rates.

Effective application demonstrated on clinical trial data.

Abstract

Estimating individualized treatment rules is a central task for personalized medicine. [zhao2012estimating] and [zhang2012robust] 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 an inverse probability of censoring weighting or a semiparametric modeling of the censoring and failure times as done in [zhao2015doubly]. To accomplish this, we take advantage of the tree based approach proposed in [zhu2012recursively] 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.