A general joint model for longitudinal measurements and competing risks survival data with heterogeneous random effects |
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Authors: | Xin Huang Gang Li Robert M Elashoff Jianxin Pan |
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Institution: | 1.Amgen Inc.,South San Francisco,USA;2.Department of Biostatistics, School of Public Health,University of California at Los Angeles,Los Angeles,USA;3.Department of Biomathematics,University of California at Los Angeles,Los Angeles,USA;4.School of Mathematics,The University of Manchester,Manchester,UK |
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Abstract: | 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. |
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Keywords: | |
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