Abstract
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.
| Original language | English |
|---|---|
| Pages (from-to) | 1465-1475 |
| Number of pages | 11 |
| Journal | Statistics in Medicine |
| Volume | 22 |
| Issue number | 9 |
| DOIs | |
| State | Published - May 15 2003 |
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Keywords
- Dementia studies
- Informative missing
- Longitudinal data
- Stochastic model
ASJC Scopus subject areas
- Epidemiology
Cite this
An illness-death stochastic model in the analysis of longitudinal dementia data. / Harezlak, Jaroslaw; Gao, Sujuan; Hui, Siu.
In: Statistics in Medicine, Vol. 22, No. 9, 15.05.2003, p. 1465-1475.Research output: Contribution to journal › Article
}
TY - JOUR
T1 - An illness-death stochastic model in the analysis of longitudinal dementia data
AU - Harezlak, Jaroslaw
AU - Gao, Sujuan
AU - Hui, Siu
PY - 2003/5/15
Y1 - 2003/5/15
N2 - 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.
AB - 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.
KW - Dementia studies
KW - Informative missing
KW - Longitudinal data
KW - Stochastic model
UR - http://www.scopus.com/inward/record.url?scp=0038702132&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=0038702132&partnerID=8YFLogxK
U2 - 10.1002/sim.1506
DO - 10.1002/sim.1506
M3 - Article
C2 - 12704610
AN - SCOPUS:0038702132
VL - 22
SP - 1465
EP - 1475
JO - Statistics in Medicine
JF - Statistics in Medicine
SN - 0277-6715
IS - 9
ER -