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Markov chain models and estimation of absolute progression rates: application to cataract progression in diabetic
T C Prevost1, T E Rohan, S W Duffy
1MRC Biostatistics Unit, Institute of Public Health, Cambridge, UK.
Background:
We present a case study in the use of Markov chain models of disease progression, with exponential regression to model the effects of covariates.
Methods:
An exponential regression model was developed for a three-state Markov chain to model progression of cataracts in diabetic patients, with a view to estimation of absolute progression rates. Two methods of estimation were applied, a non-linear least squares approximation, and Markov Chain Monte Carlo (MCMC).
Results:
Both methods gave estimated transition rates which can readily be transformed to absolute progression probabilities. Agreement was reasonable for most but not all of the parameters.
Conclusions:
The MCMC estimates had more conservative variance estimates.
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