Joint modeling of longitudinal data with a dependent terminal event

ABSTRACT

Longitudinal data often arise in longitudinal follow-up studies, and there may exist a dependent terminal event such as death that stops the follow-up. In this article, we propose a new joint modeling for the analysis of longitudinal data with informative observation times via a dependent terminal event and two latent variables. Estimating equations are developed for parameter estimation, and asymptotic properties of the resulting estimators are established. In addition, a generalization of the joint model with time-varying coefficients for the longitudinal response variable is considered, and goodness-of-fit methods for assessing the adequacy of the model are also provided. The proposed method works well in our simulation studies, and is applied to a data set from a bladder cancer study.

Keywords:

• Estimating equations
• Informative observation times
• Joint modeling
• Latent variables
• Terminal event
• Time-varying coefficient

MATHEMATICS SUBJECT CLASSIFICATION:

• Primary 62N02
• Secondary 62G20, 62N01.

Additional information

Funding

The first author's research was partly supported by International Cooperation Projects (2010DFA31790) of the Chinese Ministry of Science and Technology. The third author's research was partly supported by the National Natural Science Foundation of China Grants (nos. 11231010, 11171330, and 11021161) and Key Laboratory of RCSDS, CAS (no. 2008DP173182).