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Regression models for unbalanced longitudinal ordinal data: computer software and a simulation study.

Kai Fun Yu1, Weishi Yuan

  • 1National Institute of Child Health and Human Development, Department of Health and Human Services, Building 6100, Room 7B05, Bethesda, MD 20892, USA. yukf@mail.nih.gov

Computer Methods and Programs in Biomedicine
|July 22, 2004
PubMed
Summary
This summary is machine-generated.

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A new SAS macro, GGOREX, enhances the analysis of longitudinal ordinal data. Simulation studies validate GGOREX, offering a robust tool for researchers studying health and developmental outcomes.

Area of Science:

  • Biostatistics
  • Statistical Software Development
  • Longitudinal Data Analysis

Background:

  • Analysis of longitudinal ordinal data presents unique statistical challenges.
  • Existing methods like GEECAT and GEEGOR provide a foundation for such analyses.
  • The need for validated computational tools is crucial in health and developmental research.

Purpose of the Study:

  • To develop and validate GGOREX, a SAS macro for analyzing longitudinal ordinal data.
  • To extend previous work on generalized estimating equations for correlated ordinal responses.
  • To provide a reliable computational tool for researchers in fields like child health.

Main Methods:

  • Development of GGOREX as a SAS macro, extending GEECAT and GEEGOR.
  • Utilization of preliminary data from a National Institute of Child Health and Human Development study for illustration.

Related Experiment Videos

  • Creation of simulation programs to validate the GGOREX procedure in finite samples.
  • Development of a FORTRAN program using IMSL to compute probability distributions for ordinal responses.

    • Main Results:

    • GGOREX is developed as an extension of existing SAS macros for longitudinal ordinal data analysis.
    • A simulation study framework is established to assess the finite sample performance of GGOREX.
    • The computational procedure for determining the probability distribution of ordinal responses is implemented.

    Conclusions:

    • GGOREX offers an advanced computational tool for the analysis of longitudinal ordinal data.
    • The developed simulation methods provide a means to validate the statistical procedures implemented in GGOREX.
    • The availability of GGOREX and its validation framework supports rigorous research in developmental and health sciences.