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Calibrating longitudinal cognition in Alzheimer's disease across diverse test batteries and datasets

Gross, Alden L., Sherva, Richard, Mukherjee, Shubhabrata, Newhouse, Stephen, Kauwe, John S.K., Munsie, Leanne M., Waterston, Leo B., Bennett, David A., Jones, Richard N., Green, Robert C., Crane, Paul K. and Williams, Julie 2014. Calibrating longitudinal cognition in Alzheimer's disease across diverse test batteries and datasets. Neuroepidemiology 43 (3-4) , pp. 194-205. 10.1159/000367970

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Background: We sought to identify optimal approaches by calibrating longitudinal cognitive performance across studies with different neuropsychological batteries. Methods: We examined four approaches to calibrate cognitive performance in nine longitudinal studies of Alzheimer's disease (AD) (n = 10,875): (1) common test, (2) standardize and average available tests, (3) confirmatory factor analysis (CFA) with continuous indicators, and (4) CFA with categorical indicators. To compare precision, we determined the minimum sample sizes needed to detect 25% cognitive decline with 80% power. To compare criterion validity, we correlated cognitive change from each approach with 6-year changes in average cortical thickness and hippocampal volume using available MRI data from the AD Neuroimaging Initiative. Results: CFA with categorical indicators required the smallest sample size to detect 25% cognitive decline with 80% power (n = 232) compared to common test (n = 277), standardize-and-average (n = 291), and CFA with continuous indicators (n = 315) approaches. Associations with changes in biomarkers changes were the strongest for CFA with categorical indicators. Conclusions: CFA with categorical indicators demonstrated greater power to detect change and superior criterion validity compared to other approaches. It has wide applicability to directly compare cognitive performance across studies, making it a good way to obtain operational phenotypes for genetic analyses of cognitive decline among people with AD. i 2014 S. Karger AG, Basel

Item Type: Article
Date Type: Published Online
Status: Published
Schools: Medicine
Additional Information: Julie Williams is a member of the GENAROAD Consortium
Publisher: Karger
ISSN: 0251-5350
Date of Acceptance: 23 August 2014
Last Modified: 27 Oct 2020 15:00

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