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Residual-based tree-structured survival analysis.

Sündüz Keleş1, Mark R Segal

  • 1Group in Biostatistics, University of California, Berkeley, CA 94720, USA.

Statistics in Medicine
|January 10, 2002
PubMed
Summary
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This study establishes an analytic link between logrank and martingale residual sum-of-squares for tree-structured regression, explaining tree similarities in survival analysis. This aids in handling censored data more effectively.

Area of Science:

  • Statistics
  • Biostatistics
  • Machine Learning

Background:

  • Non-parametric regression techniques are extended for right-censored survival data.
  • Direct methods handle censoring, but residual-based approaches offer an alternative using standard regression.

Purpose of the Study:

  • To develop analytic correspondences for tree-structured regression with censored survival data.
  • To establish a relationship between logrank and martingale residual sum-of-squares split functions.

Main Methods:

  • Developing analytic correspondences for tree-structured regression.
  • Investigating the relationship between logrank and martingale residual sum-of-squares split functions.
  • Utilizing simulation and a real-world example (time to AIDS data).

Related Experiment Videos

Main Results:

  • An analytic relationship between logrank and martingale residual sum-of-squares split functions is provided.
  • This relationship explains the observed similarity in trees generated by both methods.
  • Simulations and an AIDS data example support the findings.

Conclusions:

  • The established analytic correspondence clarifies the behavior of tree-structured regression in survival analysis.
  • This work enhances understanding and application of non-parametric methods for censored data.
  • The findings are applicable to time-to-event data analysis in various fields.