title: A functional decline model for prevalent cohort data.
abstract: Longitudinal designs are often used for studying the natural history of diseases. Data sets typically consist of short series of repeated measures on prevalent cases. We propose a growth model approach to the analysis of follow-up data to describe functional decline and associated risk factors in disease progression. We illustrate the model with an application to longitudinal data that describe the time-evolution of cognitive decline in a cohort of patients with Alzheimer's disease.
authors.affiliation: HIV Center for Clinical and Behavioral Studies, New York State Psychiatric Institute, NY, USA.
authors.forename: X
authors.initials: X
authors.lastname: Liu
authors.forename: W Y
authors.initials: WY
authors.lastname: Tsai
authors.forename: Y
authors.initials: Y
authors.lastname: Stern
mesh_terms: D000544:Alzheimer Disease; D003072:Cognition Disorders; D015331:Cohort Studies; D018450:Disease Progression; D006801:Humans; D008137:Longitudinal Studies; D017711:Nonlinear Dynamics; D011569:Psychiatric Status Rating Scales; D012307:Risk Factors; D013269:Stochastic Processes; D013997:Time Factors