Analyzing Recurrent Marker Data by Forward, Backward, and Time-Adjusted Models in the Presence of Terminal Events

• Activity Number: 370
• Type: Invited
• Date/Time: Tuesday, August 5, 2014 : 2:00 PM to 3:50 PM
• Sponsor: Section on Risk Analysis
• Author(s): Mei-Cheng Wang*+
• Companies: Johns Hopkins University
• Keywords: Backward process ; Forward process ; Markers ; Point process ; Recurrent events ; Terminal event

Abstract

Recurrent events and marker data arise in many follow-up and surveillance studies where the observation is ended by a terminal event or censoring. In the study the three different types of outcome (recurrent events, marker and time to terminal event) are possibly correlated. We consider modeling and estimation for such data by forward, backward and time-adjusted models: (i) Forward recurrent marker process starts at a time origin, 0, and moves forward along time t in the conventional way. (ii) Backward recurrent marker process considers the terminal event as the time origin and counts time backward. (iii) Time-adjusted models characterize the association of recurrent events, markers and time to terminal event by rescaling the time units with some additional adjustments. In this talk we will compare the three different types of models, discuss statistical challenges for each model, and present some methods and data applications.