Supplementary Material for: Calibrating Longitudinal Cognition in Alzheimer's Disease Across Diverse Test Batteries and Datasets

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.