Purpose: Dementia algorithms are often developed in cross-sectional samples but implemented in longitudinal studies to ascertain incident dementia. However, algorithm performance may be higher in cross-sectional settings, and this may impact estimates of risk factor associations.
Methods: We used data from the Religious Orders Study and the Memory and Aging Project (N = 3460) to assess the performance of example algorithms in classifying prevalent dementia in cross-sectional samples versus incident dementia in longitudinal samples. We used an applied example and simulation study to characterize the impact of varying sensitivity, specificity, and unequal sensitivity or specificity between exposure groups (differential performance) on estimated hazard ratios from Cox models.
Results: Using all items, algorithm sensitivity was higher for prevalent (0.796) versus incident dementia (0.719); hazard ratios had slight bias. Sensitivity differences were larger using a subset of items (0.732 vs. 0.600) and hazard ratios were 13%-19% higher across adjustment sets compared to estimates using gold-standard dementia status. Simulations indicated specificity and differential algorithmic performance between exposure groups may have large effects on hazard ratios.
Conclusions: Algorithms developed using cross-sectional data may be adequate for longitudinal settings when performance is high and non-differential. Poor specificity or differential performance between exposure groups may lead to biases.
Keywords: Algorithms; Bias; Dementia; Measurement; Risk factors.
Copyright © 2022. Published by Elsevier Inc.