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Recursive Partitioning Method on Competing Risk Outcomes.

Wei Xu1, Jiahua Che2, Qin Kong2

  • 1Department of Biostatistics, Princess Margaret Cancer Centre, Toronto, ON, Canada.; Dalla Lana School of Public Health, University of Toronto, Toronto, ON, Canada.

Cancer Informatics
|August 4, 2016
PubMed
Summary

Researchers developed a new recursive partitioning method for cancer studies to analyze competing risks like recurrence-free survival, aiding prognostic and predictive modeling.

Keywords:
Cox proportional hazards modelclinical cancer outcomescompeting risk outcomesparametric competing risk modelprognostic and predictive effectrecursive partitioning algorithmsurvival tree model

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Area of Science:

  • Biostatistics
  • Cancer Research
  • Clinical Trials

Background:

  • Analyzing competing risks in cancer clinical studies is crucial for understanding outcomes like recurrence-free survival.
  • Existing methods may not adequately address the complexities of prognostic and predictive modeling with competing risks.

Purpose of the Study:

  • To develop a novel recursive partitioning framework for constructing prognostic and predictive models using competing risk data.
  • To define specific algorithms for splitting rules, pruning, and tree selection tailored for competing risk scenarios.

Main Methods:

  • The proposed framework integrates both semiparametric (Cox proportional hazards model) and parametric competing risk models.
  • It includes specific splitting rules, a pruning algorithm, and a tree selection algorithm for competing risk tree models.
  • Prognostic and predictive tree models are developed to account for potential confounding factors.

Main Results:

  • Extensive simulations demonstrated that the developed methods exhibit well-controlled Type I error rates.
  • The methods showed robust statistical power performance in analyzing competing risk data.
  • The framework was applied to a retrospective oropharyngeal cancer patient study for prognostic tree development.

Conclusions:

  • The novel recursive partitioning framework offers a flexible approach for prognostic and predictive modeling in the presence of competing risks.
  • The methodology effectively handles confounding factors and demonstrates reliable performance in simulations and a real-world clinical study.