Summary
Objectives:
To demonstrate the application of Markov models, especially for ordinal outcomes,
within the context of regression models for correlated data.
Methods:
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.
Results:
Both models, though not directly comparable, provide a similar conclusion. The application
of these models with standard statistical software is straightforward.
Conclusions:
Markov models can be valuable alternatives to random effects modes or procedures
based on generalized estimation equations.
Keywords
Markov model - proportional odds model - continuation ratio model - generalized estimation
equations - random effects
