Robust Estimation for Longitudinal Data with Informative Observation Times

• Activity Number: 327
• Type: Invited
• Date/Time: Tuesday, August 11, 2015 : 10:30 AM to 12:20 PM
• Sponsor: Biometrics Section
• Author(s): Xingqiu Zhao* and Kin-yat Liu
• Companies: The Hong Kong Polytechnic University and The Hong Kong Polytechnic University
• Keywords: Estimating equation ; Informative observation Process ; Longitudinal data ; Model checking ; Robust estimation

Abstract

In this paper, we focus on regression analysis of irregularly observed longitudinal data that often occur in medical follow-up studies and observational investigations. The analysis of these data involves two processes. One is the underlying recurrent event process of interest and the other is the observation process that controls observation times. Most of the existing methods, however, rely on some restrictive models or assumptions such as the Poisson assumption. For this, we propose a class of more flexible joint models and a robust estimation approach for regression analysis of longitudinal data with related observation times. The asymptotic properties of the proposed estimators are established and a model checking procedure is also presented. The numerical studies indicate that the proposed methods work well for practical situations.