Analyzing longitudinal data with informative observation times under biased sampling

Abstract

In many situations, longitudinal responses may be correlated with observation times as well as censoring time. This paper considers the regression analysis of longitudinal data where these correlations may exist under biased sampling, and a joint modeling approach that uses some latent variables to characterize the correlations is proposed. For inference about regression parameters, estimating equation approaches are developed and asymptotic properties of the proposed estimators are established. The finite sample behavior of the methods is examined through simulation studies, and an application to a data set from a bladder cancer study is provided for illustration.