Modeling Alzheimer's Disease Biomarkers' Trajectory in the Absence of a Gold Standard Using a Bayesian Approach.

To advance our understanding of Alzheimer's Disease (AD), especially during the preclinical stage when patients' brain functions are mostly intact, recent research has shifted towards studying AD biomarkers across the disease continuum. A widely adopted framework in AD research, proposed by Jack and colleagues, maps the progression of these biomarkers from the preclinical stage to symptomatic stages, linking their changes to the underlying pathophysiological processes of the disease. However, most existing studies rely on clinical diagnoses as a proxy for underlying AD status, potentially overlooking early stages of disease progression where biomarker changes occur before clinical symptoms appear. In this work, we develop a novel Bayesian approach to directly model the underlying AD status as a latent disease process and biomarker trajectories as nonlinear functions of disease progression. This allows for more data-driven exploration of AD progression, reducing potential biases due to inaccurate clinical diagnoses. We address the considerable heterogeneity among individuals' biomarker measurements by introducing a subject-specific latent disease trajectory as well as incorporating random intercepts to further capture additional inter-subject differences in biomarker measurements. We evaluate our model's performance through simulation studies. Applications to the Alzheimer's Disease Neuroimaging Initiative (ADNI) study yield interpretable clinical insights, illustrating the potential of our approach in facilitating the understanding of AD biomarker evolution.