  Progressive cognitive decline spanning across decades is characteristic of
Alzheimer's disease (AD). Various predictive models have been designed to
realize its early onset and study the long-term trajectories of cognitive test
scores across populations of interest. Research efforts have been geared
towards superimposing patients' cognitive test scores with the long-term
trajectory denoting gradual cognitive decline, while considering the
heterogeneity of AD. Multiple trajectories representing cognitive assessment
for the long-term have been developed based on various parameters, highlighting
the importance of classifying several groups based on disease progression
patterns. In this study, a novel method capable of self-organized prediction,
classification, and the overlay of long-term cognitive trajectories based on
short-term individual data was developed, based on statistical and differential
equation modeling. We validated the predictive accuracy of the proposed method
for the long-term trajectory of cognitive test score results on two cohorts:
the Alzheimer's Disease Neuroimaging Initiative (ADNI) study and the Japanese
ADNI study. We also presented two practical illustrations of the simultaneous
evaluation of risk factor associated with both the onset and the longitudinal
progression of AD, and an innovative randomized controlled trial design for AD
that standardizes the heterogeneity of patients enrolled in a clinical trial.
These resources would improve the power of statistical hypothesis testing and
help evaluate the therapeutic effect. The application of predicting the
trajectory of longitudinal disease progression goes beyond AD, and is
especially relevant for progressive and neurodegenerative disorders.
