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Longitudinal Models of Growth and Survival Applied to the Early Detection of Alzheimer’s DiseaseDepartment of Psychology, University of Southern California, Los Angeles, jmcardle{at}usc.edu
School of Aging Studies, University of South Florida, Tampa
Aging Research Center, Division of Geriatric Epidemiology, Neurotec, Karolinska Institute, Stockholm, Sweden, Max Planck Institute for Human Development, Center for Lifespan Psychology, Berlin, Germany
Aging Research Center, Division of Geriatric Epidemiology, Neurotec, Karolinska Institute, Stockholm, Sweden This article explores new statistical methodologies for using longitudinal data in the early prediction of Alzheimer’s disease (AD). Specifically, the authors examine some new techniques that allow the joint or "shared" estimation of longitudinal components based on both duration (survival) and quantitative changes (growth curves). These new shared growth-survival parameter models may be used to characterize the declining functions that anticipate the onset of AD. The authors apply these models to data from the Kungsholmen Project, a longitudinal study of aging in Stockholm, Sweden. They examine age-based survival-frailty models for the onset of AD, latent growth-decline curve models for changes in cognition over age, and 3 alternative forms of models for the shared relationships of survival and early cognitive decline. The accuracy and reliability of this approach is considered for a better understanding of the developmental course of AD in these data, including the potential removal of biases due to subject selection.
Key Words: early prediction • Alzheimer’s disease • growth-survival modeling • shared-parameter models
Journal of Geriatric Psychiatry and Neurology, Vol. 18, No. 4,
234-241 (2005) This article has been cited by other articles:
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