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Tree-structured proportional hazards regression modeling

H Ahn1, W Y Loh

  • 1Department of Statistics, University of Wisconsin-Madison 53706-1685.

Biometrics
|June 1, 1994
PubMed
Summary
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This study introduces a new method for analyzing survival data using piecewise proportional hazards models. It employs recursive stratification and bootstrapping to ensure accurate model fitting and serve as a goodness-of-fit test.

Area of Science:

  • Biostatistics
  • Survival Analysis
  • Statistical Modeling

Background:

  • Censored survival data analysis is crucial in many fields.
  • Proportional hazards models are widely used but require careful validation.
  • Assessing model fit for complex survival data remains a challenge.

Purpose of the Study:

  • To present a novel method for fitting piecewise proportional hazards models.
  • To introduce a robust approach for stratifying censored survival data.
  • To provide a formal goodness-of-fit test for proportional hazards models.

Main Methods:

  • Recursive stratification using statistical tests and residual analysis.
  • Application of the bootstrap method to control Type I error rates.
  • Fitting piecewise proportional hazards models to censored data.

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Main Results:

  • The proposed method effectively fits piecewise proportional hazards models.
  • Recursive stratification successfully identifies relevant strata.
  • The bootstrap ensures the Type I error probability remains controlled.
  • The method functions as a formal goodness-of-fit test.

Conclusions:

  • The developed method offers a reliable approach for analyzing censored survival data.
  • Recursive stratification combined with bootstrapping enhances proportional hazards model fitting.
  • This method provides a valuable tool for model validation in survival analysis.