Taking account of between-patient variability when modeling decline in Alzheimer's disease.
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.
Joseph
L
L
Department of Epidemiology and Biostatistics, McGill University, Montreal General Hospital, Quebec, Canada.
Wolfson
D B
DB
Bélisle
P
P
Brooks
J O
JO
Mortimer
J A
JA
Tinklenberg
J R
JR
Yesavage
J A
JA
D000544:Alzheimer Disease / Q000453:epidemiology*; D001499:Bayes Theorem; D002140:California / Q000453:epidemiology; D018450:Disease Progression; D006801:Humans; D008137:Longitudinal Studies; D008910:Minnesota / Q000453:epidemiology; D015233:Models, Statistical; D018579:Patient Selection