Joint Analysis of Longitudinal Data With Informative Observation Times and a Dependent Terminal Event

Abstract

In many longitudinal studies, repeated measures are often correlated with observation times. Also, there may exist a dependent terminal event such as death that stops the follow-up. In this article, we propose a new joint model for the analysis of longitudinal data in the presence of both informative observation times and a dependent terminal event via latent variables. Estimating equation approaches are developed for parameter estimation, and the resulting estimators are shown to be consistent and asymptotically normal. In addition, some graphical and numerical procedures are presented for model checking. Simulation studies demonstrate that the proposed method performs well for practical settings. An application to a medical cost study of chronic heart failure patients from the University of Virginia Health System is provided.

KEY WORDS:

• Dependent observation times
• Estimating equations
• Informative drop-out
• Joint modeling
• Latent variables
• Longitudinal Medical Cost

Acknowledgments

Liuquan Sun’s research was partly supported by the National Natural Science Foundation of China grants (nos. 11171330, 10971015, 10731010, and 11021161) and the Key Laboratory of RCSDS, CAS (no. 2008DP173182). Xinyuan Song’s research was supported by GRF 404711 and 446609 from the Hong Kong Special Administration Region. Lei Liu’s research was partly supported by the National Institutes of Health/National Institute on Alcohol Abuse and Alcoholism (NIH/NIAAA) grant RC1 AA019274 and the Agency for Healthcare Research and Quality (AHRQ) grant R01 HS020263. The authors thank the editor, Professor Xuming He, an associate editor, and three referees for their insightful comments and suggestions that greatly improved the article. The authors also thank Dr Jason Lyman, Mr Mac Dent, and Mr Ken Scully at the Clinical Data Repository of the University of Virginia for data preparation.