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Multi‐state models and diabetic retinopathy
Author(s) -
Marshall Guillermo,
Jones Richard H.
Publication year - 1995
Publication title -
statistics in medicine
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.996
H-Index - 183
eISSN - 1097-0258
pISSN - 0277-6715
DOI - 10.1002/sim.4780141804
Subject(s) - diabetic retinopathy , retinopathy , medicine , markov chain , disease , diabetes mellitus , proportional hazards model , markov process , computer science , mathematics , statistics , endocrinology , machine learning
This paper discusses the application of a multi‐state model to diabetic retinopathy under the assumption that a continuous time Markov process determines the transition times between disease stages. The multi‐state model consists of three transient states that represent the early stages of retinopathy, and one final absorbing state that represent the irreversible stage of retinopathy. By using a model with covariables, we explore the effects of factors that influence the onset, progression, and regression of diabetic retinopathy among subjects with insulin‐dependent diabetes mellitus. We can also introduce time‐dependent covariables in the model by assuming that the covariables remain constant between two observations. We can also obtain survival‐type curves from each stage of the disease and for any combination of patient risk factors.