title: A Bayesian semiparametric multivariate joint model for multiple longitudinal outcomes and a time-to-event.
abstract: Motivated by a real data example on renal graft failure, we propose a new semiparametric multivariate joint model that relates multiple longitudinal outcomes to a time-to-event. To allow for greater flexibility, key components of the model are modelled nonparametrically. In particular, for the subject-specific longitudinal evolutions we use a spline-based approach, the baseline risk function is assumed piecewise constant, and the distribution of the latent terms is modelled using a Dirichlet Process prior formulation. Additionally, we discuss the choice of a suitable parameterization, from a practitioner's point of view, to relate the longitudinal process to the survival outcome. Specifically, we present three main families of parameterizations, discuss their features, and present tools to choose between them.
authors.affiliation: Department of Biostatistics, Erasmus Medical Center, P.O. Box 2040, 3000 CA Rotterdam, The Netherlands. d.rizopoulos@erasmusmc.nl
authors.forename: Dimitris
authors.initials: D
authors.lastname: Rizopoulos
authors.forename: Pulak
authors.initials: P
authors.lastname: Ghosh
mesh_terms: D001499:Bayes Theorem; D005260:Female; D005919:Glomerular Filtration Rate; D006084:Graft Rejection / Q000473:pathology; D006400:Hematocrit; D006801:Humans; D016030:Kidney Transplantation / Q000473:pathology; D008137:Longitudinal Studies; D008297:Male; D008390:Markov Chains; D008954:Models, Biological; D015233:Models, Statistical; D009010:Monte Carlo Method; D009716:Numerical Analysis, Computer-Assisted; D011507:Proteinuria; D016019:Survival Analysis