A Latent-Class Model for Time-To-Event Outcomes and High-Dimensional Imaging Data

Structural magnetic resonance imaging (MRI) is one of the primary predictors of Alzheimer's disease risk, enabling the identification of patients with similar risk profiles for precision medicine treatment. Motivated by the need for flexible modeling in AD research, we propose a latent-class model that addresses the heterogeneity within study populations. This model allows for varying relationships between covariates and survival outcomes, accommodating the dynamics of AD progression. The imaging predictors are characterized by bivariate splines over triangulation to accommodate the irregular domain of the brain images. We develop a generalized expectation-maximization (EM) algorithm that combines the computational methods for logistic regression and penalized proportional hazards models to implement the proposed approach. We demonstrate the advantages of the proposed method through extensive simulation studies and provide an application to the Alzheimer's Disease Neuroimaging Initiative (ADNI) study, which helps to reveal different subtypes or stages of the disease process in Alzheimer's Disease.