Abstract
Longitudinal data often arise when subjects are followed over a period of time, and in many situations, there may exist informative observation times and a dependent terminal event such as death that stops the follow-up. In this article, we propose joint modeling and analysis of longitudinal data with possibly informative observation times and a dependent terminal event in which a common subject-specific latent variable is used to characterize the correlations. A borrow-strength estimation procedure is developed for parameter estimation, and both large-sample and finite-sample properties of the proposed estimators are established. In addition, some goodness-of-fit methods for assessing the adequacy of the model are provided. An application to a bladder cancer study is illustrated.
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