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

• Published in: American journal of epidemiology Vol. 149; no. 10; pp. 963 - 973
• Main Authors: Joseph, L., Wolfson, D. B., Bélisle, P., Brooks, J. O., Mortimer, J. A., Tinklenberg, J. R., Yesavage, J. A.
• Format: Journal Article
• Language: English
• Published: Cary, NC Oxford University Press 15.05.1999
• Subjects: Alzheimer Disease - epidemiology; Alzheimer's disease; Bayes Theorem; Biological and medical sciences; California - epidemiology; Degenerative and inherited degenerative diseases of the nervous system. Leukodystrophies. Prion diseases; Disease Progression; Epidemiology; General aspects; Gibbs sampler; hierarchical model; Humans; Longitudinal Studies; Medical sciences; Methodology; Minnesota - epidemiology; Models, Statistical; natural history; Neurology; Patient Selection; Public health. Hygiene; Public health. Hygiene-occupational medicine; random effects model; statistical; United States; North America; America; Minnesota; California; Human; Nervous system diseases; Methodology; Alzheimer disease; Statistical model; Central nervous system disease; Degenerative disease; Epidemiology; Public health; Cerebral disorder
• ISSN: 0002-9262; 1476-6256
• DOI: 10.1093/oxfordjournals.aje.a009741

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.