Modeling the History of Diabetic Retinopathy

Modeling the History of Diabetic Retinopathy

Bruce A. Craig and Michael A. Newton

Case Studies in Bayesian Statistics III. C. Gatsonis et al. (eds). New York: Springer-Verlag, 305--323, 1997.


The Wisconsin Epidemiologic Study of Diabetic Retinopathy (WESDR) is a population based study to investigate prevalence, incidence, and progression of diabetic retinopathy. To analyze the young-onset insulin-dependent subpopulation of this study, we propose and fit a hidden Markov model. A nonhomogeneous discrete-time Markov chain describes the natural course of the disease. We also account for complicating factors such as treatment intervention and death. The model is formulated on a yearly basis so as to correspond with a common interval for physician visits. Bayesian inference is used to combine the WESDR data with the model. Because the health status of each subject was observed at several separated years, there are many unobserved variables. Markov chain Monte Carlo is used to simulate the posterior. Predictive distributions are discussed as a prognostic tool to assist researchers in evaluating costs and benefits of interventions.