Document Detail

A general joint model for longitudinal measurements and competing risks survival data with heterogeneous random effects.
MedLine Citation:
PMID:  20549344     Owner:  NLM     Status:  MEDLINE    
This article studies a general joint model for longitudinal measurements and competing risks survival data. The model consists of a linear mixed effects sub-model for the longitudinal outcome, a proportional cause-specific hazards frailty sub-model for the competing risks survival data, and a regression sub-model for the variance-covariance matrix of the multivariate latent random effects based on a modified Cholesky decomposition. The model provides a useful approach to adjust for non-ignorable missing data due to dropout for the longitudinal outcome, enables analysis of the survival outcome with informative censoring and intermittently measured time-dependent covariates, as well as joint analysis of the longitudinal and survival outcomes. Unlike previously studied joint models, our model allows for heterogeneous random covariance matrices. It also offers a framework to assess the homogeneous covariance assumption of existing joint models. A Bayesian MCMC procedure is developed for parameter estimation and inference. Its performances and frequentist properties are investigated using simulations. A real data example is used to illustrate the usefulness of the approach.
Xin Huang; Gang Li; Robert M Elashoff; Jianxin Pan
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Publication Detail:
Type:  Comparative Study; Journal Article     Date:  2010-06-12
Journal Detail:
Title:  Lifetime data analysis     Volume:  17     ISSN:  1572-9249     ISO Abbreviation:  Lifetime Data Anal     Publication Date:  2011 Jan 
Date Detail:
Created Date:  2011-01-13     Completed Date:  2011-06-01     Revised Date:  2014-04-02    
Medline Journal Info:
Nlm Unique ID:  9516348     Medline TA:  Lifetime Data Anal     Country:  United States    
Other Details:
Languages:  eng     Pagination:  80-100     Citation Subset:  IM    
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MeSH Terms
Bayes Theorem*
Longitudinal Studies / methods*
Markov Chains
Models, Statistical
Monte Carlo Method
Proportional Hazards Models*
Sensitivity and Specificity
Survival Analysis
Grant Support
P01 AT003960/AT/NCCAM NIH HHS; P01 AT003960-01A1/AT/NCCAM NIH HHS; P30 CA016042/CA/NCI NIH HHS; P30 CA016042-35/CA/NCI NIH HHS

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