A General Framework for Joint Multi-State Models
Researchers have developed a novel general framework for joint multi-state modeling that integrates longitudinal biomarker data with complex time-to-event processes. This framework allows for nonlinear longitudinal submodels and utilizes stochastic gradient descent for scalable inference, accommodating both Markovian and semi-Markovian transition structures. The approach enables dynamic prediction of individualized state-transition probabilities and personalized risk assessments, as demonstrated through simulations and an application to the PAQUID cohort. AI