A comparison of ordinal logistic regression models using Classical and Bayesian approaches in an analysis of factors associated with diabetic retinopathy. Issue 12 (9th September 2016)

• Record Type: Journal Article
• Authors: Vaitheeswaran, K.; Subbiah, M.; Ramakrishnan, R.; Kannan, T.
• Is Part Of: Journal of applied statistics. Volume 43:Issue 12(2016)
• Page Start: 2254
• Page End: 2260
• Publication Date: 2016-09-09
• Subjects: AIC -- diabetic retinopathy -- DIC -- link functions -- ordinal logistic regression; Statistics -- Periodicals; 519.5
• DOI: 10.1080/02664763.2016.1140725
• Languages: English
• ISSNs: 0266-4763

Abstract

ABSTRACT: Estimating the risk factors of a disease such as diabetic retinopathy (DR) is one of the important research problems among bio-medical and statistical practitioners as well as epidemiologists. Incidentally many studies have focused in building models with binary outcomes, that may not exploit the available information. This article has investigated the importance of retaining the ordinal nature of the response variable (e.g. severity level of a disease) while determining the risk factors associated with DR. A generalized linear model approach with appropriate link functions has been studied using both Classical and Bayesian frameworks. From the result of this study, it can be observed that the ordinal logistic regression with probit link function could be more appropriate approach in determining the risk factors of DR. The study has emphasized the ways to handle the ordinal nature of the response variable with better model fit compared to other link functions.