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American Journal of Epidemiology Vol. 149, No. 10: 963-973
Copyright © 1999 by The Johns Hopkins University School of Hygiene and Public Health

other

Taking Account of Between-Patient Variability When Modeling Decline in Alzheimer's Disease

L. Joseph1,2, D. B. Wolfson3, P. Bélisle1, J. O. Brooks, III4, J. A. Mortimer5, J. R. Tinklenberg6 and J. A. Yesavage6

1Department of Epidemiology and Biostatistics, McGill University Montreal, Quebec, Canada
2Division of Clinical Epidemiology, Department of Medicine, Montreal General Hospital Montreal, Quebec, Canada
3Department of Mathematics and Statistics, McGill University Montreal, Quebec, Canada
4Department of Psychiatry, Stanford University School of Medicine Stanford CA
5Institute on Aging, University of South Florida Tampa, FL
6Veterans Affairs Health Care System, Palo Alto Division, Stanford University School of Medicine Stanford, CA

The pattern of deterioration in patients with Alzheimer's disease is highly variable within a given population. With recent speculation that the apolipoprotein E allele may influence rate of decline and claims that certain drugs may slow the course of the disease, there is a compelling need for sound statistical methodology to address these questions. Current statistical methods for describing decline do not adequately take into account between-patient variability and possible floor and/or ceiling effects in the scale measuring decline, and they fail to allow for uncertainty in disease onset. In this paper, the authors analyze longitudinal Mini-Mental State Examination scores from two groups of Alzheimer's disease subjects from Palo Alto, California, and Minneapolis, Minnesota, in 1981–1993 and 1986–1988, respectively. A Bayesian hierarchical model is introduced as an elegant means of simultaneously overcoming all of the difficulties referred to above. Am J Epidemiol 1999; 149:963–73.

Alzheimer's disease; Gibbs sampler; hierarchical model; natural history; models, statistical; random effects model


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