期刊
STATISTICS IN MEDICINE
卷 22, 期 9, 页码 1465-1475出版社
JOHN WILEY & SONS LTD
DOI: 10.1002/sim.1506
关键词
longitudinal data; stochastic model; informative missing; dementia studies
类别
资金
- NIA NIH HHS [P30 AG010133, R01 AG009956, R01 AG 15813, R01 AG 09956, R01 AG015813, P30 AG 10133, R01 AG015813-01A1] Funding Source: Medline
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 (C) 2003 John Wiley Sons, Ltd.
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