A Multimodal Disease Progression Model for Genetic Associations with Disease Dynamics

We introduce a disease progression model suited for neurodegenerative pathologies that allows to model associations between covariates and dynamic features of the disease course. We establish a statistical framework and implement an algorithm for its estimation. We show that the model is reliable and can provide uncertainty estimates of the discovered associations thanks to its Bayesian formulation. The model's interest is showcased by shining a new light on genetic associations.