TY - JOUR
T1 - Joint analysis of longitudinal data with informative observation times and a dependent terminal event
AU - Sun, Liuquan
AU - Song, Xinyuan
AU - Zhou, Jie
AU - Liu, Lei
N1 - Funding Information:
Liuquan Sun is Professor, Institute of Applied Mathematics, Academy of Mathematics and Systems Science, Chinese Academy of Sciences (CAS), Beijing 100190, China (E-mail: [email protected]). Xinyuan Song is Associate Professor, Department of Statistics, The Chinese University of Hong Kong, Hong Kong, China (E-mail: [email protected]). Jie Zhou is Ph.D. candidate, Institute of Applied Mathematics, Academy of Mathematics and Systems Science, Chinese Academy of Sciences (CAS), Beijing 100190, China (E-mail: [email protected]). Lei Liu is Associate Professor, Department of Public Health Sciences, University of Virginia, Charlottesville, VA 22908-0717, USA (E-mail: [email protected]). 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.
PY - 2012
Y1 - 2012
N2 - 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.
AB - 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.
KW - Dependent observation times
KW - Estimating equations
KW - Informative drop-out
KW - Joint modeling
KW - Latent variables
KW - Longitudinal medical cost
UR - http://www.scopus.com/inward/record.url?scp=84864395606&partnerID=8YFLogxK
U2 - 10.1080/01621459.2012.682528
DO - 10.1080/01621459.2012.682528
M3 - Article
AN - SCOPUS:84864395606
SN - 0162-1459
VL - 107
SP - 688
EP - 700
JO - Journal of the American Statistical Association
JF - Journal of the American Statistical Association
IS - 498
ER -