Estimation of bivariate measurements having different change points, with application to cognitive ageing

Longitudinal studies of ageing make repeated observations of multiple measurements on each subject. Change point models are often used to model longitudinal data. We demonstrate the use of Bayesian and profile likelihood methods to simultaneously estimate different change points in the longitudinal...

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Bibliographic Details
Published in:Statistics in medicine 2001-12, Vol.20 (24), p.3695-3714
Main Authors: Hall, Charles B., Ying, Jun, Kuo, Lynn, Sliwinski, Martin, Buschke, Herman, Katz, Mindy, Lipton, Richard B.
Format: Article
Language:English
Subjects:
Aged
Aged, 80 and over
Aging - physiology
Alzheimer Disease - pathology
Bayes Theorem
Biological and medical sciences
Cognition - physiology
Computerized, statistical medical data processing and models in biomedicine
Degenerative and inherited degenerative diseases of the nervous system. Leukodystrophies. Prion diseases
Humans
Intelligence - physiology
Likelihood Functions
Longitudinal Studies
Medical sciences
Medical statistics
Memory - physiology
Models, Biological
Neurology
New York City
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Summary:Longitudinal studies of ageing make repeated observations of multiple measurements on each subject. Change point models are often used to model longitudinal data. We demonstrate the use of Bayesian and profile likelihood methods to simultaneously estimate different change points in the longitudinal course of two different measurements of cognitive function in subjects in the Bronx Aging Study who developed Alzheimer's disease (AD). Analyses show that accelerated memory decline, as measured by Buschke Selective Reminding, begins between seven and eight years before diagnosis of AD, while decline in performance on speeded tasks as measured by WAIS Performance IQ begins slightly more than two years before diagnosis, significantly after the decline in memory. Copyright © 2001 John Wiley & Sons, Ltd.
ISSN:0277-6715
1097-0258
DOI:10.1002/sim.1113