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A FORTRAN program for multivariate survival analysis on the personal computer.

P G Mulder1

  • 1Institute of Biostatistics, Erasmus University, Rotterdam, The Netherlands.

Computer Methods and Programs in Biomedicine
|September 1, 1988
PubMed
Summary
This summary is machine-generated.

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This study introduces a FORTRAN program for competing risks regression analysis. The program models failure rates using log-linear functions and explanatory variables, offering an efficient alternative to existing models.

Area of Science:

  • Biostatistics
  • Survival Analysis
  • Computational Statistics

Background:

  • Multivariate survival analysis is crucial for understanding complex failure processes.
  • Competing risks scenarios, where multiple events can lead to failure, require specialized analytical approaches.
  • Existing models may not always be computationally efficient or flexible enough for diverse datasets.

Purpose of the Study:

  • To present a FORTRAN program for multivariate survival regression in competing risks.
  • To model relevant failure rates using log-linear functions of explanatory variables.
  • To offer a computationally efficient alternative to Cox's proportional hazards model.

Main Methods:

  • Development of a FORTRAN program for competing risks regression.

Related Experiment Videos

  • Modeling failure rates as a log-linear function of explanatory variables, including time-dependent covariates.
  • Utilizing the Newton-Raphson method for maximum likelihood estimation of coefficients.

    • Main Results:

    • The program effectively performs multivariate survival regression in competing risks.
    • Log-linear modeling of failure rates provides flexibility with various explanatory variables.
    • Maximum likelihood estimates are efficiently obtained via the Newton-Raphson method.
    • The program demonstrates acceptable running times on personal computers, even for large datasets.

    Conclusions:

    • The presented FORTRAN program offers a robust and efficient tool for competing risks survival analysis.
    • The log-linear modeling approach provides a flexible and powerful alternative to traditional methods.
    • The software is suitable for practical application in biostatistics and related fields.