Taban Baghfalaki (The University of Manchester) – Dynamic Risk Prediction by Combining Zero-Inflated Longitudinal Data and Survival Outcomes with a Cure Fraction

We propose a joint modeling framework that integrates zero-inflated longitudinal count data with time-to-event outcomes, explicitly accounting for a cure fraction. The longitudinal process is modeled using flexible mixed-effects Hurdle models to handle excess zeros and overdispersion, while the survival component combines a Cox model with a mixture cure formulation to distinguish susceptible and cured individuals. The two processes are linked through current longitudinal information, enabling dynamic risk prediction. Inference is performed using Hamiltonian Monte Carlo for robust estimation. We validate the approach through simulations and apply it to an HIV cohort, demonstrating its value for personalized risk assessment and clinical decision-making.