Mixed effect models of longitudinal Alzheimer's disease data: a cautionary note

Longitudinal studies of cognitive function in Alzheimer's disease (AD) patients are powerful tools to better understand the biology and natural history of the disease, but the attributes of the studies that make them valuable also pose special challenges to analysts. A fundamental problem is the accurate measure of time at which cognitive decline begins. Investigators typically use the date of AD diagnosis or the date of enrollment in an AD study. If the rate of cognitive decline is non-linear, variables associated with the time of diagnosis or enrollment might artificially be associated with the rate of decline. Unlike the mixed effects models typically used to analyse cognitive decline, summary measure analyses do not directly compare the rate of decline with time since decline began, and, therefore, are less sensitive to biased measures of time of decline. We simulated trajectories of cognitive decline using the multivariate normal random effect model and tested the ability of the two analytic techniques to discriminate between true and spurious associations. Our analyses suggest summary measure models are less likely to detect spurious associations generated by biased measures of time at which decline begins, and more likely to detect true associations concealed by biased time measurement.