Markov chain models and estimation of absolute progression rates: application to cataract progression in diabetic adults.

T. C. Prevost, T. E. Rohan, S. W. Duffy, H. H. Chen, T. To, R. D. Hill

Research output: Contribution to journalArticle

3 Scopus citations

Abstract

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.

Original languageEnglish (US)
Pages (from-to)337-344
Number of pages8
JournalJournal of epidemiology and biostatistics
Volume4
Issue number4
StatePublished - 1999
Externally publishedYes

ASJC Scopus subject areas

  • Epidemiology

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