A Dynamic Mover-Stayer Model for Recurrent Event Processes Subject to Resolution

• Activity Number: 194
• Type: Contributed
• Date/Time: Monday, August 5, 2013 : 10:30 AM to 12:20 PM
• Sponsor: Biometrics Section
• Author(s): Hua Shen*+ and Hua Shen and Richard Cook
• Companies: University of Waterloo and University of Waterloo and University of Waterloo
• Keywords: Dynamic mover-stayer model ; EM algorithm ; Recurrent event data ; Semiparametric estimation

Abstract

In studies of affective disorder, individuals are often observed to experience recurrent symptomatic exacerbations warranting hospitalization. Interest may lie in modeling the occurrence of such exacerbations over time and identifying associated risk factors. In some patients, recurrent exacerbations are temporally clustered following disease onset, but cease to occur after a period of time. We develop a dynamic mover-stayer model in which a canonical binary variable associated with each event indicates whether the underlying disease has resolved. An individual whose disease process has not resolved will experience events following a standard point process model governed by a latent intensity. When the disease process resolves, the complete data intensity becomes zero and no further event will occur. An expectation-maximization (EM) algorithm is described for parametric and semiparametric model fitting based on a discrete time dynamic mover-stayer model and a latent intensity-based model of the underlying point process.