Markov Models for Repeated Ordinal Data

Abstract

To demonstrate the application of Markov models, especially for ordinal outcomes, within the context of regression models for correlated data. A brief review of regression methods for correlated data is given. A proportional odds model and a continuation ratio model is applied to repeated measurements of macular pigment density, obtained in an intervention study on the supplementation of macular carotenoids. The correlation between repeated assessments is assumed to follow a first-order Markov model. The models are implemented with standard statistical software. Both models, though not directly comparable, provide a similar conclusion. The application of these models with standard statistical software is straightforward. Markov models can be valuable alternatives to random effects modes or procedures based on generalized estimation equations.