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MIXREG: a computer program for mixed-effects regression analysis with autocorrelated errors

D Hedeker1, R D Gibbons

  • 1Division of Epidemiology and Biostatistics, School of Public Health, University of Illinois at Chicago 60612-7260, USA. HEDEKER@UIC.EDU

Computer Methods and Programs in Biomedicine
|May 1, 1996
PubMed
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MIXREG software estimates mixed-effects regression models (MRM) for longitudinal and clustered data with complex error structures. It handles unbalanced data and estimates dependency, adjusting for clustering effectively.

Area of Science:

  • Statistics
  • Biostatistics
  • Econometrics

Background:

  • Longitudinal and clustered data analysis presents challenges due to unbalanced measurements and inherent dependencies.
  • Mixed-effects regression models (MRM) are powerful tools for such data, but require specialized software for complex error structures.

Purpose of the Study:

  • To introduce MIXREG, a software program designed for estimating mixed-effects regression models.
  • To provide a flexible tool for analyzing normally-distributed response data with autocorrelated errors and clustered structures.

Main Methods:

  • MIXREG employs maximum marginal likelihood estimation, incorporating the EM algorithm and a Fisher-scoring solution.
  • It handles general forms of autocorrelated errors (AR(1), MA(1), ARMA(1,1)) and estimates the degree of dependency in clustered data.

Related Experiment Videos

  • Empirical Bayes methods are used for estimating individual random effects.

    • Main Results:

    • The program successfully provides estimates for mixed-effects regression models with autocorrelated errors.
    • It allows for the analysis of unbalanced longitudinal data where individuals have varying numbers or timings of measurements.
    • MIXREG adjusts for data clustering by jointly estimating dependency parameters.

    Conclusions:

    • MIXREG is a comprehensive software solution for advanced mixed-effects regression modeling.
    • It offers robust estimation for complex data structures, including longitudinal and clustered data with autocorrelated errors.