title: A Bayesian analysis of bivariate ordinal data: Wisconsin epidemiologic study of diabetic retinopathy revisited.
abstract: In many biomedical experiments one may often encounter bivariate data which are component-wise ordinal. The data set of the ophthalmologic experiment of the Wisconsin Epidemiologic Study of Diabetic Retinopathy (WESDR) is an example of such data. Several authors considered the analysis of such data from different viewpoints. The present work reviews the existing literature based on the WESDR data and on the basis of some latent variables provide the technique for analysing such data more easily in a Bayesian framework. Computation supports the methodology to a great extent. A comparison between our approach and the likelihood based approach considered by Kim has also been made.
authors.affiliation: Applied Statistics Unit, Indian Statistical Institute, 203 B.T. Road, Calcutta-700 035, India. atanu@isical.ac.in
authors.forename: Atanu
authors.initials: A
authors.lastname: Biswas
authors.forename: Kalyan
authors.initials: K
authors.lastname: Das
mesh_terms: D000328:Adult; D017668:Age of Onset; D001499:Bayes Theorem; D003922:Diabetes Mellitus, Type 1 / Q000453:epidemiology / Q000473:pathology; D003930:Diabetic Retinopathy / Q000453:epidemiology*; D005260:Female; D006801:Humans; D008297:Male; D008954:Models, Biological; D015233:Models, Statistical; D009716:Numerical Analysis, Computer-Assisted; D014922:Wisconsin / Q000453:epidemiology