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Related Experiment Videos

MIXOR: a computer program for mixed-effects ordinal regression analysis

D Hedeker1, R D Gibbons

  • 1Division of Epidemiology and Biostatistics (M/C 922), School of Public Health and Prevention Research Center, University of Illinois at Chicago, IL 60612-7260, USA.

Computer Methods and Programs in Biomedicine
|March 1, 1996
PubMed
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MIXOR software offers maximum marginal likelihood estimates for mixed-effects models, analyzing clustered or longitudinal data with ordinal outcomes. It adjusts for data dependency and estimates varying effects over time.

Area of Science:

  • Biostatistics
  • Statistical Software
  • Longitudinal Data Analysis

Background:

  • Mixed-effects models are crucial for analyzing complex data structures like clustered or longitudinal designs.
  • Ordinal outcomes are common in various scientific fields, requiring specialized regression techniques.
  • Existing methods may not adequately address the dependency within clusters or individual variations over time.

Purpose of the Study:

  • To introduce MIXOR, a software tool for estimating mixed-effects ordinal regression models.
  • To provide methods for analyzing dichotomous and ordinal outcomes in clustered and longitudinal data.
  • To enable the estimation of dependency within clusters and individual-varying effects over time.

Main Methods:

  • Utilizes marginal maximum likelihood estimation via a Fisher-scoring algorithm.

Related Experiment Videos

  • Estimates the Cholesky factor of the random-effects variance-covariance matrix.
  • Applies to ordinal probit, logistic, and complementary log-log regression models.

    • Main Results:

    • MIXOR provides accurate maximum marginal likelihood estimates for specified mixed-effects models.
    • The software effectively adjusts for data dependency arising from clustering.
    • It allows for the estimation of individual-varying intercepts and slopes in longitudinal analyses.

    Conclusions:

    • MIXOR is a valuable tool for researchers analyzing clustered or longitudinal data with ordinal outcomes.
    • The software enhances the analysis of complex dependency structures and time-varying effects.
    • It offers a robust approach to mixed-effects ordinal regression modeling.