This is the program for the 2010 Joint Statistical Meetings in Vancouver, British Columbia.

Abstract Details

Activity Number: 530
Type: Contributed
Date/Time: Wednesday, August 4, 2010 : 10:30 AM to 12:20 PM
Sponsor: Biometrics Section
Abstract - #308313
Title: Analysis of Longitudinal Data with Time-Varying Dependence on Observation Times
Author(s): Na Cai*+ and Wenbin Lu and Hao Helen Zhang
Companies: North Carolina State University and North Carolina State University and North Carolina State University
Address: 704 Ryan Ct., Raleigh, NC, 27606,
Keywords: time-varying dependence ; latent variable ; estimating equation ; joint model

In analysis of longitudinal data, the longitudinal outcomes are often assumed to be independent of observation times. In practice, however, the observation time process may be informative and correlated with the longitudinal outcomes. In this article, we propose a joint model, in which the correlation between the outcomes and observation times is very flexible and allowed to be time dependent. Under the model, censoring times can be correlated with both the longitudinal outcomes and observation times via a common latent variable. A two-stage estimation procedure is developed to estimate the parameters of primary interest. The resulting estimators are proved to be consistent and asymptotically normal. Simulation studies and a real data analysis are reported for evaluating the proposed method.

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