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

Yifan Cui1, Ruoqing Zhu2, Michael Kosorok3

  • 1Department of Statistics and Operations Research, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA.

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Summary

This study introduces a novel method for personalized medicine, extending outcome weighted learning for survival data. The new approach improves individualized treatment rule estimation for censored data, outperforming existing techniques in simulations.

Keywords:
Excess value boundIndividualized treatment ruleNonparametric estimationOutcome weighted learningRecursively imputed survival treesRight censored data

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Area of Science:

  • Biostatistics
  • Clinical Trials
  • Personalized Medicine

Background:

  • Estimating individualized treatment rules is crucial for personalized medicine.
  • Existing outcome weighted learning methods do not directly handle right-censored survival data.
  • Previous approaches often require inverse probability of censoring weighting or complex modeling.

Purpose of the Study:

  • To extend outcome weighted learning for estimating individualized treatment rules with right-censored survival data.
  • To avoid the need for inverse probability of censoring weighting or semiparametric modeling.
  • To develop robust estimators for personalized treatment strategies.

Main Methods:

  • Utilized a tree-based approach for nonparametric imputation of survival times.
  • Developed two imputation strategies: replacing rewards with expected survival time and imputing censored observations with conditional expected failure times.
  • Established consistency and convergence rates for the proposed estimators.

Main Results:

  • The proposed estimators demonstrated improved performance compared to existing methods in simulation studies.
  • The method successfully handles right-censored survival data without complex modeling assumptions.
  • Consistency and convergence rates were theoretically established for the new estimators.

Conclusions:

  • The extended outcome weighted learning approach provides a robust method for estimating individualized treatment rules with censored survival data.
  • This method offers a valuable tool for personalized medicine, particularly in clinical trial settings.
  • The approach was successfully illustrated on a non-small cell lung cancer clinical trial.