Markov chain models and estimation of absolute progression rates: application to cataract progression in diabetic adults.
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.
Prevost
T C
TC
MRC Biostatistics Unit, Institute of Public Health, Cambridge, UK.
Rohan
T E
TE
Duffy
S W
SW
Chen
H H
HH
To
T
T
Hill
R D
RD
D000368:Aged; D002386:Cataract / Q000209:etiology*; D003922:Diabetes Mellitus, Type 1 / Q000150:complications*; D003924:Diabetes Mellitus, Type 2 / Q000150:complications*; D018450:Disease Progression; D005260:Female; D006801:Humans; D008297:Male; D008390:Markov Chains; D015233:Models, Statistical; D009010:Monte Carlo Method