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

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Abstract

MIXOR provides maximum marginal likelihood estimates for mixed-effects ordinal probit, logistic, and complementary log-log regression models. These models can be used for analysis of dichotomous and ordinal outcomes from either a clustered or longitudinal design. For clustered data, the mixed-effects model assumes that data within clusters are dependent. The degree of dependency is jointly estimated with the usual model parameters, thus adjusting for dependence resulting from clustering of the data. Similarly, for longitudinal data, the mixed-effects approach can allow for individual-varying intercepts and slopes across time, and can estimate the degree to which these time-related effects vary in the population of individuals. MIXOR uses marginal maximum likelihood estimation, utilizing a Fisher-scoring solution. For the scoring solution, the Cholesky factor of the random-effects variance-covariance matrix is estimated, along with the effects of model covariates. Examples illustrating usage and features of MIXOR are provided.

References (37)

  • R.D. Bock

    Measurement of human variation: a two stage model

  • R.D. McKelvey et al.

    A statistical model for the analysis of ordinal level dependent variables

    J. Math. Sociol.

    (1975)
  • P. McCullagh

    Regression models for ordinal data (with discussion)

    J. R. Stat. Soc. Ser. B

    (1980)
  • H. Goldstein
  • A.S. Bryk et al.
  • N.T. Longford
  • N.M. Laird et al.

    Random-effects models for longitudinal data

    Biometrics

    (1982)
  • R.D. Bock

    Within-subject experimentation in psychiatric research

  • A.P. Dempster et al.

    Estimation in covariance components models

    J. Am. Stat. Assoc.

    (1981)
  • J. DeLeeuw et al.

    Random coefficient models for multilevel analysis

    J. Educ. Stat.

    (1986)
  • N.T. Longford

    A fast scoring algorithm for maximum likelihood estimation in unbalanced mixed models with nested effects

    Biometrika

    (1987)
  • J.F. Strenio et al.

    Empirical Bayes estimation of individual growth curve parameters and their relationship to covariates

    Biometrika

    (1983)
  • R.I. Jennrich et al.

    Unbalanced repeated-measures models with structured covariance matrices

    Biometrics

    (1986)
  • R.D. Bock

    The discrete Bayesian

  • R.D. Gibbons et al.

    Random regression models: A comprehensive approach to the analysis of longitudinal psychiatric data

    Psychopharmacol. Bull.

    (1988)
  • D. Hedeker et al.

    Investigating drug plasma levels and clinical response using random regression models

    Psychopharmacol. Bull.

    (1989)
  • D. Hedeker et al.

    Random regression models for multicenter clinical trials data

    Psychopharmacol. Bull.

    (1991)
  • R.D. Gibbons et al.

    Some conceptual and statistical issues in analysis of longitudinal psychiatric data

    Arch. Gen. Psychiatry

    (1993)
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