title: Markov models for repeated ordinal data.
abstract: OBJECTIVE
  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.
authors.affiliation: University of Münster, Institute of Epidemiology and Social Medicine, Domagkstr. 3, 48149 Münster, Germany. wellmann@uni-muenster.de
authors.forename: J
authors.initials: J
authors.lastname: Wellmann
mesh_terms: D002338:Carotenoids / Q000008:administration & dosage* / Q000032:analysis; D019587:Dietary Supplements; D018450:Disease Progression; D006801:Humans; D016015:Logistic Models; D008137:Longitudinal Studies; D008268:Macular Degeneration / Q000473:pathology / Q000517:prevention & control*; D008390:Markov Chains; D009885:Ophthalmology / Q000706:statistics & numerical data; D017063:Outcome Assessment, Health Care / Q000379:methods*; D016016:Proportional Hazards Models; D012168:Retinal Pigments / Q000737:chemistry; D012984:Software; D013997:Time Factors
chemical_list: D012168:Retinal Pigments; D002338:Carotenoids