Estimating hidden morbidity via its effect on mortality and disability.
The applicability of the theory of partially observed finite-state Markov processes to the study of disease, morbidity, and disability is explored. A method is developed for the continuous updating of parameter estimates over time in longitudinal studies analogous to Kalman filtering in continuous valued continuous time stochastic processes. It builds on a model of filtering of incompletely observed finite-state Markov processes subject to mortality due to Yashin et al. The method of estimation is based on maximum likelihood theory and the incompleteness in the observation of the process is dealt with by applying missing information principles in maximum likelihood estimation.
Woodbury
M A
MA
Department of Community and Family Medicine, Duke University Medical Center, Durham, NC 27706.
Manton
K G
KG
Yashin
A I
AI
D000368:Aged; D002908:Chronic Disease / Q000453:epidemiology* / Q000401:mortality; D006801:Humans; D008137:Longitudinal Studies; D008175:Lung Neoplasms / Q000401:mortality; D008297:Male; D008390:Markov Chains; D008875:Middle Aged; D008954:Models, Biological; D011336:Probability; D012307:Risk Factors; D012907:Smoking; D014481:United States