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An illness–death stochastic model in the analysis of longitudinal dementia data
Author(s) -
Harezlak Jaroslaw,
Gao Sujuan,
Hui Siu L.
Publication year - 2003
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.1506
Subject(s) - dementia , markov chain , disease , medicine , epidemiology , random effects model , longitudinal data , missing data , statistics , longitudinal study , hazard , econometrics , demography , computer science , mathematics , data mining , meta analysis , chemistry , organic chemistry , sociology
A significant source of missing data in longitudinal epidemiological studies on elderly individuals is death. Subjects in large scale community‐based longitudinal dementia studies are usually evaluated for disease status in study waves, not under continuous surveillance as in traditional cohort studies. Therefore, for the deceased subjects, disease status prior to death cannot be ascertained. Statistical methods assuming deceased subjects to be missing at random may not be realistic in dementia studies and may lead to biased results. We propose a stochastic model approach to simultaneously estimate disease incidence and mortality rates. We set up a Markov chain model consisting of three states, non‐diseased, diseased and dead, and estimate the transition hazard parameters using the maximum likelihood approach. Simulation results are presented indicating adequate performance of the proposed approach. Copyright © 2003 John Wiley & Sons, Ltd.