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

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 wit...

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Main Authors: Lang Wu, Wei Liu, Grace Y. Yi, Yangxin Huang
Format: Article
Language:English
Published: Wiley 2012-01-01
Series:Journal of Probability and Statistics
Online Access:http://dx.doi.org/10.1155/2012/640153
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author Lang Wu
Wei Liu
Grace Y. Yi
Yangxin Huang
author_facet Lang Wu
Wei Liu
Grace Y. Yi
Yangxin Huang
author_sort Lang Wu
collection DOAJ
description 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.
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institution OA Journals
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1687-9538
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spelling doaj-art-908108df8751408b8e9f8bed5586d7302025-08-20T02:09:44ZengWileyJournal of Probability and Statistics1687-952X1687-95382012-01-01201210.1155/2012/640153640153Analysis of Longitudinal and Survival Data: Joint Modeling, Inference Methods, and IssuesLang Wu0Wei Liu1Grace Y. Yi2Yangxin Huang3Department of Statistics, The University of British Columbia, Vancouver, BC, V6T 1Z2, CanadaDepartment of Mathematics and Statistics, York University, Toronto, ON, M3J 1P3, CanadaDepartment of Statistics and Actuarial Science, University of Waterloo, Waterloo, ON, N2L 3G1, CanadaDepartment of Epidemiology and Biostatistics, College of Public Health, University of South Florida, Tampa, FL 33612, USAIn 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.http://dx.doi.org/10.1155/2012/640153
spellingShingle Lang Wu
Wei Liu
Grace Y. Yi
Yangxin Huang
Analysis of Longitudinal and Survival Data: Joint Modeling, Inference Methods, and Issues
Journal of Probability and Statistics
title Analysis of Longitudinal and Survival Data: Joint Modeling, Inference Methods, and Issues
title_full Analysis of Longitudinal and Survival Data: Joint Modeling, Inference Methods, and Issues
title_fullStr Analysis of Longitudinal and Survival Data: Joint Modeling, Inference Methods, and Issues
title_full_unstemmed Analysis of Longitudinal and Survival Data: Joint Modeling, Inference Methods, and Issues
title_short Analysis of Longitudinal and Survival Data: Joint Modeling, Inference Methods, and Issues
title_sort analysis of longitudinal and survival data joint modeling inference methods and issues
url http://dx.doi.org/10.1155/2012/640153
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AT weiliu analysisoflongitudinalandsurvivaldatajointmodelinginferencemethodsandissues
AT graceyyi analysisoflongitudinalandsurvivaldatajointmodelinginferencemethodsandissues
AT yangxinhuang analysisoflongitudinalandsurvivaldatajointmodelinginferencemethodsandissues