  Alzheimer's Disease (AD) research has shifted to focus on biomarker
trajectories and their potential use in understanding the underlying AD-related
pathological process. A conceptual framework was proposed in modern AD research
that hypothesized biomarker cascades as a result of underlying AD pathology. In
this paper, we leveraged this idea to jointly model AD biomarker trajectories
as a function of the latent AD disease progression with individual and
covariate effects in the latent disease progression model and the biomarker
cascade. We tailored our methods to address a number of real-data challenges
that are often present in AD studies. Simulation studies were performed to
investigate the proposed approach under various realistic but less-than-ideal
situations. Finally, we illustrated the methods using real data from the
BIOCARD and the ADNI studies. The analyses investigated cascading patterns of
AD biomarkers in these datasets and presented prediction results for
individual-level profiles over time. These findings highlight the potential of
the conceptual biomarker cascade framework to be leveraged for diagnosis and
monitoring.
