While changes over time in the MMSE due to dementia have been studied, the relationship between MMSE scores and the duration of disease course is less well understood. For this purpose we used the interval between consecutive MMSE assessments as the time factor. We also investigated the impact of sex, education and age at testing on the resulting model. Such estimations of disease course are useful when comparing different populations for both clinical and research purposes. 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 enrolment in an AD study.
If the rate of cognitive decline is non-linear, variables associated with the time of diagnosis or enrolment 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. An ideal measure of global functioning for patients with dementia would discriminate at very high and very low levels of functioning and would have linear measurement properties such that a given change in score corresponds to the same amount of change in underlying ability at any part of the ability continuum.