Modeling a Progressive Disease Process Under Panel Observation

• Activity Number: 403
• Type: Contributed
• Date/Time: Tuesday, July 31, 2012 : 2:00 PM to 3:50 PM
• Sponsor: Biometrics Section
• Author(s): Amy Laird*+ and Lurdes Y.T. Inoue
• Companies: University of Washington and University of Washington
• Keywords: longitudinal panel data ; Markov model ; semi-Markov process

Abstract

Longitudinal studies are a useful tool for investigating the course of chronic diseases. Many chronic diseases are progressive and can be characterized by a set of health states. We can gain a greater understanding of the disease process by modeling the sequence of visited states and the length of time spent in each state. The major modeling challenge is that the transition times are not known exactly under panel observation. Existing modeling approaches either impose strong parametric assumptions on the sojourn times in each state, or model time discretely and carry out inference nonparametrically, but both approaches have drawbacks. We propose an alternative modeling approach that uses the principle of data augmentation, which has a number of advantages over existing approaches. We evaluate performance of this method and illustrate in a longitudinal study of subjects' progression through stages of HIV and AIDS.