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• W2950481883 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 drop-out in quantile regression. A general 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, and parametric and non-parametric assumptions for the baseline hazard. Model properties are illustrated through a simulation study and an application to an original data set about dilated cardiomyopathies." @default.
• W2950481883 created "2019-06-27" @default.
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• W2950481883 date "2014-04-04" @default.
• W2950481883 modified "2023-10-14" @default.
• W2950481883 title "Longitudinal quantile regression in presence of informative drop-out through longitudinal-survival joint modeling" @default.
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• W2950481883 hasPublicationYear "2014" @default.
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