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GFREG: a computer program for maximum likelihood regression using the Generalized F distribution.

S A Hogg, A Ciampi

    Computer Methods and Programs in Biomedicine
    |July 1, 1985
    PubMed
    Summary
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    This study introduces a FORTRAN program for flexible survival data analysis using the Generalized F distribution. It enables robust regression parameter estimation and covariate selection for censored survival times.

    Area of Science:

    • Biostatistics
    • Statistical Computing
    • Survival Analysis

    Background:

    • Censored survival data analysis is crucial in biostatistics.
    • Existing methods may lack flexibility in modeling diverse error distributions.
    • Accurate regression parameter estimation and covariate selection are essential for reliable results.

    Purpose of the Study:

    • To present a FORTRAN program for maximum likelihood estimation within the Generalized F family of distributions.
    • To facilitate regression parameter estimation in log-linear models for censored survival times with covariates.
    • To offer a flexible tool for analyzing survival data with various error distribution shapes.

    Main Methods:

    • Maximum likelihood estimation using the Generalized F distribution.

    Related Experiment Videos

  • Generalized reduced gradient algorithm for optimization.
  • Stepwise variable search algorithm for covariate selection.

    • Main Results:

    • The program estimates regression parameters for log-linear models with censored survival times.
    • It accommodates a wide range of error distribution shapes, including common biostatistical distributions.
    • Includes model selection criteria, parameter estimates, standard errors, quantile/survival rates, residuals, and plots.

    Conclusions:

    • The developed FORTRAN program provides a versatile and powerful tool for survival data analysis.
    • It enhances the ability to model complex survival data with flexible error distributions.
    • The program's comprehensive output aids in model interpretation and validation.