Regression Analysis of Longitudinal Data in the Presence of Informative Observation and Censoring Times

Abstract

Longitudinal data frequently occur in many studies, such as longitudinal follow-up studies. To develop statistical methods and theory for the analysis of these data, independent or noninformative observation and censoring times are typically assumed, which naturally leads to inference procedures conditional on observation and censoring times. But in many situations this may not be true or realistic; that is, longitudinal responses may be correlated with observation times as well as censoring times. This article considers the analysis of longitudinal data where these correlations may exist and proposes a joint modeling approach that uses some latent variables to characterize the correlations. For inference about regression parameters, estimating equation approaches are developed and both large-sample and final-sample properties of the proposed estimators are established. In addition, some graphical and numerical procedures are presented for model checking. The methodology is applied to a bladder cancer study that motivated this investigation.

KEY WORDS:

• Estimating equation
• Informative observation process
• Joint modeling
• Latent variables