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Latent-variable models for longitudinal data with bivariate ordinal outcomes.

David Todem1, KyungMann Kim, Emmanuel Lesaffre

  • 1Department of Epidemiology, Division of Biostatistics, Michigan State University, B601 West Fee Hall, East Lansing, MI 48823, USA. todem@msu.edu

Statistics in Medicine
|July 13, 2006
PubMed
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This study introduces a new statistical model for analyzing repeated ordinal outcomes, suitable for longitudinal psychiatric trials. The model effectively captures associations between variables and allows for both individual and population-level interpretations.

Area of Science:

  • Biostatistics
  • Longitudinal Data Analysis
  • Psychiatric Research Methodology

Background:

  • Analyzing bivariate ordinal outcomes in longitudinal studies presents statistical challenges.
  • Existing models may not adequately capture both individual-level and population-averaged effects.
  • Accurate modeling is crucial for interpreting results from clinical trials, such as those for antidepressants.

Purpose of the Study:

  • To develop a flexible statistical model for joint distributions of bivariate ordinal outcomes in longitudinal data.
  • To extend latent variable modeling to accommodate repeated measures within subjects.
  • To provide a method that allows for simultaneous population comparisons and individual-level contrasts.

Main Methods:

  • Utilized latent variables to model the joint distribution of bivariate ordinal outcomes.

Related Experiment Videos

  • Employed a linear mixed model for the bivariate latent variable, incorporating random effects for repeated observations.
  • Approximated marginal likelihood using adaptive Gaussian quadrature under missing at random assumptions.

    • Main Results:

    • The proposed model successfully derives the joint distribution for bivariate ordinal longitudinal data.
    • Cross-sectional associations are modeled via the correlation coefficient of the latent variable, conditional on random effects.
    • Fixed effects parameters offer both subject-specific and population-averaged interpretations upon scaling.

    Conclusions:

    • The developed latent variable model is well-suited for analyzing bivariate ordinal longitudinal data, particularly in psychiatric trials.
    • The methodology effectively balances the need for population-level comparisons with individual-level contrasts.
    • Demonstrated utility through application to the Fluvoxamine antidepressant study data.