Estimation and sample design in prevalence surveys of dementia.
Population prevalence rates of dementia using stratified sampling have previously been estimated using two methods : standard weighted estimates and a logistic model-based approach.
An earlier study described this application of the model-based approach and reported a - small computer simulation comparing the performance of this estimator to the standard weighted estimator.
In this article we use large-scale computer simulations based on data from the recently completed Kame survey of prevalent dementia in the Japanese-American residents of King County, Washington, to describe the performance of these estimators.
We found that the standard weighted estimator was unbiased.
This estimator performed well for a sample design with proportional allocation, but performed poorly for a sample design that included large strata that were lightly sampled.
The logistic model-based estimator performed consistently well for all sample designs considered in terms of the extent of variability in estimation, although some modest bias was observed.
Mots-clés Pascal : Démence Alzheimer, Méthode statistique, Modèle mathématique, Epidémiologie, Prévalence, Estimation, Méthode calcul, Homme, Système nerveux pathologie, Système nerveux central pathologie, Encéphale pathologie, Maladie dégénérative
Mots-clés Pascal anglais : Alzheimer disease, Statistical method, Mathematical model, Epidemiology, Prevalence, Estimation, Computing method, Human, Nervous system diseases, Central nervous system disease, Cerebral disorder, Degenerative disease
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Inist-CNRS - Institut de l'Information Scientifique et Technique
Cote : 99-0339769
Code Inist : 002B17G. Création : 14/12/1999.