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GGDMLE: a computer program which finds maximum likelihood estimates for the generalized log gamma distribution.

S A Hogg, A Ciampi, J Lawless

    Computer Programs in Biomedicine
    |December 1, 1982
    PubMed
    Summary
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    This study introduces a FORTRAN program for analyzing survival data using the generalized log gamma (GLG) model. The program efficiently estimates parameters for censored data and various GLG model variations.

    Area of Science:

    • Biostatistics
    • Survival Analysis
    • Computational Statistics

    Background:

    • Survival time data analysis is crucial in many scientific fields.
    • The generalized log gamma (GLG) model offers a flexible framework for survival data.
    • Existing methods may lack efficiency in estimating parameters for complex survival models, especially with censored data.

    Purpose of the Study:

    • To develop and describe a FORTRAN program for maximum likelihood estimation (MLE) in the generalized log gamma model.
    • To facilitate the analysis of survival time data, including regression variables and censored observations.
    • To enable the study of various special cases of the GLG model by allowing fixed parameters.

    Main Methods:

    • Implementation of a FORTRAN program utilizing a modified Powell's hybrid method for optimization.

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  • The shape parameter is fixed during optimization, allowing for efficient MLE computation.
  • Inclusion of a subroutine for calculating initial parameter estimates to aid the optimization process.

    • Main Results:

    • The program successfully computes maximum likelihood estimates for the generalized log gamma model.
    • It handles regression variables linearly related to log survival time and accommodates censored data.
    • The software allows for the estimation of multiple shape parameter values in a single run and supports studying specific GLG model variants.

    Conclusions:

    • The described FORTRAN program provides an efficient tool for survival data analysis using the generalized log gamma model.
    • Its flexibility in handling parameters and censored data makes it valuable for biostatistical and computational research.
    • The program's utility is demonstrated through an application using real-world patient data.