An illness-death stochastic model in the analysis of longitudinal dementia data.
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.
Harezlak
Jaroslaw
J
Department of Biostatistics, Harvard School of Public Health, 655 Huntington Avenue, Boston, MA 02115, USA. jharezla@hsph.harvard.edu
Gao
Sujuan
S
Hui
Siu L
SL
D000368:Aged; D000369:Aged, 80 and over; D015982:Bias; D015331:Cohort Studies; D003198:Computer Simulation; D003704:Dementia / Q000453:epidemiology* / Q000401:mortality; D006801:Humans; D015994:Incidence; D008137:Longitudinal Studies; D008390:Markov Chains; D015233:Models, Statistical; D013269:Stochastic Processes