Joint modelling of multivariate markers measured repeatedly over time and clinical endpoints
Joint models for longitudinal and survival data are now widely used in biostatistics to address a variety of etiological and predictive questions. Originally designed to simultaneously analyze the trajectory of a single marker measured repeatedly over time, and the risk of a single right-censored time-to-event, joint models now need to capture increasingly complex longitudinal information to adapt to in-depth medical research questions. After an introduction of the general joint modelling methodology, I discuss two extensions proposed to handle multivariate repeated marker information: one using a latent class approach and one using a latent degradation process approach. The models are illustrated on real data, in particular to describe the progression of Multi-System Atrophy, a rare neurodegenerative disease.