Semiparametric Modeling and Estimation of the Terminal Behavior of Recurrent Marker Processes Before Failure Events

• Activity Number: 383 - Longitudinal/Repeated Measures and Terminal Events
• Type: Invited
• Date/Time: Tuesday, August 1, 2017 : 2:00 PM to 3:50 PM
• Sponsor: Lifetime Data Analysis Interest Group
• Author(s): Kwun Chuen Gary Chan* and Mei-Cheng Wang
• Companies: University of Washington and Johns Hopkins University
• Keywords: Marked Counting Process ; Partial Likelihood ; Recurrent Event Process ; Semiparametric models

Abstract

Recurrent event processes with marker measurements are mostly studied with forward time models starting from an initial event. Interestingly, the processes could exhibit important terminal behavior during a time period before occurrence of the failure event. A natural and direct way to study recurrent events prior to a failure event is to align the processes using the failure event as the time origin and to examine the terminal behavior by a backward time model. We studied regression models for backward recurrent marker processes by counting time backward from the failure event. A three-level semiparametric regression model is proposed for jointly modeling the time to a failure event, the backward recurrent event process, and the marker observed at the time of each backward recurrent event. By jointly modeling the three components, estimating equations can be constructed for marked counting processes to estimate the target parameters in the three-level regression models. The proposed models and methods are illustrated by a community-based AIDS clinical trial to examine opportunistic infections among HIV infected individuals in the last six months of life.