An illness-death stochastic model in the analysis of longitudinal dementia data.

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.

journal_name

Stat Med

journal_title

Statistics in medicine

authors

Harezlak J,Gao S,Hui SL

doi

10.1002/sim.1506

subject

Has Abstract

pub_date

2003-05-15 00:00:00

pages

1465-75

issue

9

eissn

0277-6715

issn

1097-0258

journal_volume

22

pub_type

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