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

Research output: Contribution to journalArticle

16 Citations (Scopus)

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 languageEnglish
Pages (from-to)1465-1475
Number of pages11
JournalStatistics in Medicine
Volume22
Issue number9
DOIs
StatePublished - May 15 2003

Fingerprint

Dementia
Stochastic Model
Longitudinal Studies
Missing at Random
Cohort Study
Markov Chain Model
Mortality Rate
Missing Data
Hazard
Estimate
Markov Chains
Statistical method
Surveillance
Biased
Maximum Likelihood
Information Storage and Retrieval
Incidence
Epidemiologic Studies
Cohort Studies
Mortality

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 journalArticle

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