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Journal of Probability and Statistics
Volume 2012, Article ID 640153, 17 pages
Review Article

Analysis of Longitudinal and Survival Data: Joint Modeling, Inference Methods, and Issues

1Department of Statistics, The University of British Columbia, Vancouver, BC, Canada V6T 1Z2
2Department of Mathematics and Statistics, York University, Toronto, ON, Canada M3J 1P3
3Department of Statistics and Actuarial Science, University of Waterloo, Waterloo, ON, Canada N2L 3G1
4Department of Epidemiology and Biostatistics, College of Public Health, University of South Florida, Tampa, FL 33612, USA

Received 25 August 2011; Accepted 10 October 2011

Academic Editor: Wenbin Lu

Copyright © 2012 Lang Wu et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


In the past two decades, joint models of longitudinal and survival data have received much attention in the literature. These models are often desirable in the following situations: (i) survival models with measurement errors or missing data in time-dependent covariates, (ii) longitudinal models with informative dropouts, and (iii) a survival process and a longitudinal process are associated via latent variables. In these cases, separate inferences based on the longitudinal model and the survival model may lead to biased or inefficient results. In this paper, we provide a brief overview of joint models for longitudinal and survival data and commonly used methods, including the likelihood method and two-stage methods.