Alternating Event Processes During Lifetimes: Population Dynamics and Statistical Inference

• Activity Number: 74
• Type: Contributed
• Date/Time: Sunday, July 29, 2012 : 4:00 PM to 5:50 PM
• Sponsor: Biometrics Section
• Author(s): Russell Shinohara*+ and Mei-Cheng Wang
• Companies: The Johns Hopkins University and The Johns Hopkins University
• Keywords: Incidence rate ; Prevalence probability ; Recurrent events

Abstract

Although much work has centered on univariate recurrent event processes in which the occurrence of each event is treated as a single point in time, there are many applications in which patients experience nontrivial durations associated with each event. This results in a process where the disease status of a patient alternates between exacerbations and remissions. In this work, we consider the dynamics of a chronic disease and its associated exacerbation-remission process over two time scales: calendar time and time-since-onset. In particular, over calendar time, we explore population dynamics and the relationship between incidence, prevalence and duration for such alternating event processes. We provide nonparametric estimation techniques for characteristic quantities of the process. Using a nonparametric latent variable approach to account for the relationship between the survival and alternating event processes, we develop techniques for estimating semiparametric models of prevalence. By combining population dynamics and within-process structure, the proposed approaches provide an alternative and general way to study alternating event processes.