Aaron Scheffler: Constrained covariate-dependent smoothing and curve registration with applications to disease progression modeling

Disease progression can be tracked via a cascade of changes in biomarkers and clinical measurements over the disease time course. For example, in progressive neurodegenerative diseases (ND), such as Alzheimer's Disease, changes in biomarkers (neuroanatomical images, cerebrospinal fluid) may precede clinical measurements (cognitive batteries) by months or years. Viewing repeated measurements of biomarkers and clinical measurements as a multivariate time series composed of continuous and discrete values, successful modeling of disease progression balances capturing stereotypic patterns in disease progression across subjects with subject-level variability in timing, acceleration, and shape of disease progression trajectories. A Bayesian model combining parametric and constrained semi-parametric disease progression models is proposed for curve alignment and covariate-dependent smoothing of exponential family outcomes across the disease time course, allowing for the characterization of typical disease progression as well as heterogeneity in the timing, speed, ordering, and shape of disease progression at the population-level and at the subject-level via random effects structure that partitions phase and amplitude variance. The framework will accommodate continuous and count outcomes, allowing for the incorporation of measurements ranging from neuroimaging features to sensitive sub-scales of cognitive batteries. A working example is provided from patients experiencing progressive ND.