Joint analysis of longitudinal data with additive mixed effect model for informative observation times.

Longitudinal data occur in many clinical and observational studies, and in many situations, longitudinal responses are often correlated with observation times. In this article, we propose a new joint model for the analysis of longitudinal data with informative observation times via two random effects. In particular, an additive mixed effect model is used for observation times. Estimating equation approaches are developed for parameter estimation, and asymptotic properties of the resulting estimators are established. In addition, some graphical and numerical procedures are provided for model checking. The finite-sample behavior of the proposed method is evaluated through simulation studies, and an application to a bladder cancer study is illustrated. [ABSTRACT FROM AUTHOR]