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A method for comparing semi-parametric models with parametric models in competing risks analysis.

J Wan1

  • 1University of Alabama, Birmingham 35294.

Computers and Biomedical Research, an International Journal
|December 1, 1989
PubMed
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This study compares two Cox

Area of Science:

  • Biostatistics
  • Survival Analysis
  • Clinical Trials

Background:

  • Cox's regression model is standard for survival analysis.
  • Existing models are extended for multiple failure causes.
  • Independent random censoring is a common challenge.

Purpose of the Study:

  • To analyze competing risks data with covariates under censoring.
  • To compare two distinct semi-parametric models.
  • To propose a novel comparison method using generalized variance.

Main Methods:

  • Consideration of two semi-parametric models for competing risks.
  • Development of a comparison measure based on generalized variance.
  • Application to a cancer clinical trial dataset.

Related Experiment Videos

Main Results:

  • The proposed generalized variance measure effectively compares competing risks models.
  • The method is demonstrated with a practical cancer trial example.
  • A FORTRAN program is available for implementation.

Conclusions:

  • The study provides a robust method for comparing competing risks models.
  • This facilitates better understanding of failure causes in censored data.
  • The findings are relevant for clinical trial data analysis.