Joint modeling of longitudinal and survival data

The scientific needs in modern medicine and epidemiology has evolved away from paradigms centered on single endpoints and simple cause-and-effect relationships toward a more holistic understanding of disease as a complex, dynamic system. This evolution is driven by a parallel revolution in data acquisition, where longitudinal cohort studies now generate high-frequency, high-dimensional data streams for each individual, including many biomarkers, genomic data, patient-reported outcomes, and event histories. This amount of data presents an opportunity to model the dynamics of diseases, but it also exposes the limitations of traditional statistical methodologies.