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

Lang Wu, Wei Liu, Grace Y. Yi and Yangxin Huang

Journal of Probability and Statistics, 2012, vol. 2012, 1-17

Abstract:

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.

Date: 2012
References: Add references at CitEc
Citations: View citations in EconPapers (3)

Downloads: (external link)
http://downloads.hindawi.com/journals/JPS/2012/640153.pdf (application/pdf)
http://downloads.hindawi.com/journals/JPS/2012/640153.xml (text/xml)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:hin:jnljps:640153

DOI: 10.1155/2012/640153

Access Statistics for this article

More articles in Journal of Probability and Statistics from Hindawi
Bibliographic data for series maintained by Mohamed Abdelhakeem ().