title: Longitudinal quantile regression in the presence of informative dropout through longitudinal-survival joint modeling.
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.
authors.affiliation: Department of Public Health and Infectious Diseases, Sapienza University of Rome, Piazzale Aldo Moro 5, Rome, 00186, Italy.
authors.forename: Alessio
authors.initials: A
authors.lastname: Farcomeni
authors.forename: Sara
authors.initials: S
authors.lastname: Viviani
mesh_terms: D000465:Algorithms; D015982:Bias; D056808:Biostatistics / Q000379:methods*; D002311:Cardiomyopathy, Dilated / Q000401:mortality / Q000503:physiopathology; D015331:Cohort Studies; D003198:Computer Simulation; D006304:Health Status; D006801:Humans; D016013:Likelihood Functions; D008137:Longitudinal Studies; D015233:Models, Statistical; D009010:Monte Carlo Method; D010352:Patient Dropouts / Q000706:statistics & numerical data*; D012044:Regression Analysis; D018709:Statistics, Nonparametric; D016019:Survival Analysis
keywords: joint models; longitudinal regression; quantile regression; shared-parameter models