Abstract Number:
2167
Submission Type:
Contributed Abstract
Contributed Abstract Type:
Poster
Participants:
Julia Gallini (1), Zachary Baucom (1), Yorghos Tripodis (2)
Institutions:
(1) N/A, N/A, (2) Boston University, N/A
Co-Author(s):
First Author:
Presenting Author:
Abstract Text:
Alzheimer's dementia (AD) is of increasing concern as populations attain longer and longer life spans. Prediction of conversion to AD from a cognitively normal state remains difficult and is generally poorly understood. We used state space models- specifically a factor multivariate local linear trend model- to identify latent factors of cognitive function derived from a standard battery of neuropsychological tests. Using National Alzheimer's Coordinating Center data, we performed two separate structured factor analyses in individuals who ultimately converted to dementia and individuals who did not. There was substantially higher correlation between cognitive domains in those who transitioned to dementia (range: 0.329-0.863) compared to those who did not (range: 0.087-0.202). These findings suggest a more uniform underlying cognitive process in dementia converters than in non-converters since the domains remain relatively distinct in the latter group. Next, we plan to jointly model the longitudinal factor scores with a time to dementia outcome in patients who are cognitively normal. We aim to predict risk of dementia conversion at 1, 2, and 3 years post-cognitive testing.
Keywords:
Alzheimer's disease|Factor analysis|State-space models|Joint models| |
Sponsors:
Biometrics Section
Tracks:
Longitudinal/Correlated Data
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