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• W2120486543 abstract "We propose a joint model for a time‐to‐event outcome and a quantile of a continuous response repeatedly measured over time. The quantile and survival processes are associated via shared latent and manifest variables. Our joint model provides a flexible approach to handle informative dropout in quantile regression. A Monte Carlo expectation maximization strategy based on importance sampling is proposed, which is directly applicable under any distributional assumption for the longitudinal outcome and random effects. We consider both parametric and nonparametric assumptions for the baseline hazard. We illustrate through a simulation study and an application to an original data set about dilated cardiomyopathies. Copyright © 2014 John Wiley & Sons, Ltd." @default.
• W2120486543 created "2016-06-24" @default.
• W2120486543 creator A5002519625 @default.
• W2120486543 creator A5090841446 @default.
• W2120486543 date "2014-12-09" @default.
• W2120486543 modified "2023-10-14" @default.
• W2120486543 title "Longitudinal quantile regression in the presence of informative dropout through longitudinal-survival joint modeling" @default.
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• W2120486543 doi "https://doi.org/10.1002/sim.6393" @default.
• W2120486543 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/25488110" @default.
• W2120486543 hasPublicationYear "2014" @default.
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