On the self-triggering Cox model for recurrent event data

Recurrent event data frequently occur in longitudinal studies when subjects experience more than one event during the observation period. Often, the occurrence of subsequent events is associated with the experience of previous events. Such dependence is commonly ignored in the application of standard recurrent event methodology. In this paper, we utilize a Cox-type regression model with time-varying triggering effect depending on the number and timing of previous events to enhance both model fit and prediction. Parameter estimation and statistical inference is achieved via the partial likelihood. A statistical test procedure is provided to assess the existence of the triggering effects. We demonstrate our approach via comprehensive simulation studies and a real data analysis on chronic pseudomonas infections in young cystic fibrosis patients. Our model provides significantly better predictions than standard recurrent event models.