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Estimating long‐term multivariate progression from short‐term data
Author(s) -
Donohue Michael C.,
JacqminGadda Hélène,
Le Goff Mélanie,
Thomas Ronald G.,
Raman Rema,
Gamst Anthony C.,
Beckett Laurel A.,
Jack Clifford R.,
Weiner Michael W.,
Dartigues JeanFrançois,
Aisen Paul S.
Publication year - 2014
Publication title -
alzheimer's and dementia
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 6.713
H-Index - 118
eISSN - 1552-5279
pISSN - 1552-5260
DOI - 10.1016/j.jalz.2013.10.003
Subject(s) - neuroimaging , alzheimer's disease neuroimaging initiative , term (time) , disease , multivariate statistics , time point , cognition , pathological , computer science , cognitive impairment , multivariate analysis , dementia , statistics , psychology , medicine , pathology , machine learning , mathematics , neuroscience , philosophy , physics , quantum mechanics , aesthetics
Motivation Diseases that progress slowly are often studied by observing cohorts at different stages of disease for short periods of time. The Alzheimer's Disease Neuroimaging Initiative (ADNI) follows elders with various degrees of cognitive impairment, from normal to impaired. The study includes a rich panel of novel cognitive tests, biomarkers, and brain images collected every 6 months for as long as 6 years. The relative timing of the observations with respect to disease pathology is unknown. We propose a general semiparametric model and iterative estimation procedure to estimate simultaneously the pathological timing and long‐term growth curves. The resulting estimates of long‐term progression are fine‐tuned using cognitive trajectories derived from the long‐term “Personnes Agées Quid” study. Results We demonstrate with simulations that the method can recover long‐term disease trends from short‐term observations. The method also estimates temporal ordering of individuals with respect to disease pathology, providing subject‐specific prognostic estimates of the time until onset of symptoms. When the method is applied to ADNI data, the estimated growth curves are in general agreement with prevailing theories of the Alzheimer's disease cascade. Other data sets with common outcome measures can be combined using the proposed algorithm. Availability Software to fit the model and reproduce results with the statistical software R is available as the grace package. ADNI data can be downloaded from the Laboratory of NeuroImaging.