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A Bayesian semiparametric multivariate joint model for multiple longitudinal outcomes and a time‐to‐event
Author(s) -
Rizopoulos Dimitris,
Ghosh Pulak
Publication year - 2011
Publication title -
statistics in medicine
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.996
H-Index - 183
eISSN - 1097-0258
pISSN - 0277-6715
DOI - 10.1002/sim.4205
Subject(s) - multivariate statistics , semiparametric model , computer science , piecewise , econometrics , event (particle physics) , bayesian probability , dirichlet process , semiparametric regression , flexibility (engineering) , mathematics , statistics , artificial intelligence , machine learning , nonparametric statistics , quantum mechanics , mathematical analysis , physics
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. Copyright © 2011 John Wiley & Sons, Ltd.