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Related Experiment Videos

SAS and SPLUS programs to perform Cox regression without convergence problems.

Georg Heinze1, Meinhard Ploner

  • 1Department of Medical Computer Sciences, University of Vienna, A-1090 Vienna, Spitalgasse 23, Austria. georg.heinze@akh-wien.ac.at

Computer Methods and Programs in Biomedicine
|February 21, 2002
PubMed
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Monotone likelihood in Cox regression can cause non-finite estimates. A new method, implemented in SAS and S-PLUS, provides finite solutions and interval estimation for survival data analysis.

Area of Science:

  • Biostatistics
  • Survival Analysis
  • Statistical Modeling

Background:

  • Cox regression is a standard method for survival data analysis.
  • Monotone likelihood is a data condition causing non-convergence in Cox model parameter estimates.
  • Standard maximum likelihood methods fail to address monotone likelihood issues.

Purpose of the Study:

  • To provide a practical solution for monotone likelihood in Cox regression.
  • To implement a novel procedure for obtaining finite parameter estimates.
  • To enable interval estimation and visualization of the penalized log-likelihood function.

Main Methods:

  • Development of a SAS macro and an S-PLUS library.
  • Implementation of Heinze and Schemper's procedure for monotone likelihood.

Related Experiment Videos

  • Utilizing profile penalized log-likelihood (PPL) for interval estimation.

    • Main Results:

    • The implemented method consistently yields finite parameter estimates.
    • The SAS macro and S-PLUS library successfully address monotone likelihood problems.
    • The tools facilitate interval estimation and PPL function plotting.

    Conclusions:

    • The developed SAS macro and S-PLUS library offer a robust solution for monotone likelihood in Cox regression.
    • This approach ensures reliable parameter and relative risk estimates in challenging survival datasets.
    • The availability of these tools enhances the practical application of advanced survival analysis techniques.