Subject-specific and marginal models have been developed for the analysis of longitudinal ordinal data. Subject-specific models often lack a population-average interpretation of the model parameters due to the conditional formulation of random intercepts and slopes. Marginal models frequently lack an underlying distribution for ordinal data, in particular when generalized estimating equations are applied. To overcome these issues, latent variable models underneath the ordinal outcomes with a multivariate logistic distribution can be applied. In this article, we extend the work of O'Brien and Dunson (2004), who studied the multivariate t-distribution with marginal logistic distributions. We use maximum likelihood, instead of a Bayesian approach, and incorporated covariates in the correlation structure, in addition to the mean model. We compared our method with GEE and demonstrated that it performs better than GEE with respect to the fixed effect parameter estimation when the latent variables have an approximately elliptical distribution, and at least as good as GEE for other types of latent variable distributions.
- Flexible correlation matrix
- Generalized estimating equations
- Latent variable models
- Maximum likelihood
- Multivariate logistic distribution
- Population-averaged (marginal) models