Regression Analysis of Longitudinal Data with Irregular and Informative Observation Times

• Activity Number: 243
• Type: Contributed
• Date/Time: Monday, July 30, 2012 : 2:00 PM to 3:50 PM
• Sponsor: Biometrics Section
• Author(s): Yong Chen*+ and Jing Ning and Chunyan Cai
• Companies: The University of Texas School of Public Health and MD Anderson Cancer Center and MD Anderson Cancer Center
• Keywords: Informative observation time ; Irregular observation time ; Longitudinal data analysis ; Pairwise likelihood

Abstract

In longitudinal data analyses, the observation times are often assumed to be independent of the outcomes. In applications in which this assumption is violated, the standard inferential approach of using a generalized estimating equation may lead to biased inference. In this article, we construct a novel pairwise pseudolikelihood method for longitudinal data that allows for dependence between observation times and outcomes. This method incorporates both time-dependent and time-independent covariates, while leaving the observation time process unspecified. The novelty of this method is that specification of neither the observation time process nor the covariance structure of the repeated measure process is required. Large sample properties of the regression coefficient estimates and a pseudolikelihood-ratio test statistic are established. Simulation studies demonstrate that the proposed method performs well in finite samples and is robust to the true form of the observation time process. An analysis of weight loss data from a web-based program is presented to illustrate the proposed method.