In this article, we propose a generalized estimating equations (GEE) approach for correlated
ordinal or nominal multinomial responses using a local odds ratios parameterization. Our …

The R package multgee implements the local odds ratios generalized estimating equations
(GEE) approach proposed by Touloumis et al.(2013), a GEE approach for correlated …

This thesis presents research on modelling, statistical inference and computation for
multivariate discrete data. I address the problem of how to systematically model multivariate …

In this paper, we review analytic methods for regression models for longitudinal categorical
responses. We focus on both likelihood-based approaches and non-likelihood approaches …

Analysis of data with repeated measures is often accomplished through the use of
generalized estimating equations (GEE) methodology. Although methods exist for assessing …

The method of generalized estimating equations (GEE) is often used to analyze longitudinal
and other correlated response data, particularly if responses are binary. However, few …

Generalized log-linear models can be used to describe the association structure and/or the
marginal distributions of multivariate categorical responses. We simultaneously model the …

The generalized estimating equations (GEEs) and generalized linear mixed-effects model
(GLMM) are the two most popular paradigms to extend models for cross-sectional data to a …

Moment-based methods for analyzing repeated binary responses using the marginal odds
ratio as a measure of association have been proposed by a number of authors. Carey …

In this paper a likelihood-based method for analyzing mixed discrete and continuous
regression models is proposed. We focus on marginal regression models, that is, models in …