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.
Abstract:
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.
Postscript