Joint Modeling of Biomarker Cascades Along An Unobserved Disease Progression with Differentiate Covariate Effects: An Application in Alzheimer's Disease

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.