Analysis of Longitudinal Data in the Presence of Informative Observational Times, with Application to Medical Cost Data

• Activity Number: 326
• Type: Topic Contributed
• Date/Time: Tuesday, August 5, 2014 : 10:30 AM to 12:20 PM
• Sponsor: Biometrics Section
• Author(s): Lei Liu*+ and Xuelin Huang and John O'Quigley
• Companies: Northwestern University Feinberg School of Medicine and MD Anderson Cancer Center and Université Pierre et Marie Curie - Paris VI, France
• Keywords: Frailty model ; Informative drop-out ; Longitudinal medical costs ; Proportional hazards model ; Recurrent marker

Abstract

In longitudinal observational studies, repeated measures are often taken at informative observation times. Also, there may exist a dependent terminal event such as death that stops the follow-up. For example, patients in poorer health are more likely to seek medical treatment and their medical cost for each visit tends to be higher. They are also subject to a higher mortality rate. In this article, we propose a random effects model of repeated measures in the presence of both informative observation times and a dependent terminal event. An analysis of the cost-accrual process of chronic heart failure patients from the clinical data repository at the University of Virginia Health System is presented to illustrate the proposed method.