A bayesian semiparametric multivariate joint model for multiple longitudinal outcomes and a time-to-event
Document Type
Article
Publication Title
Statistics in Medicine
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. Copyright © 2011 John Wiley & Sons, Ltd.
DOI Link
Publication Date
1-4-2011
Publisher
Wiley
Volume
Vol.30
Issue
Iss.12
Recommended Citation
Rizopoulos, Dimitris and Ghosh, Pulak, "A bayesian semiparametric multivariate joint model for multiple longitudinal outcomes and a time-to-event" (2011). Faculty Publications. 761.
https://research.iimb.ac.in/fac_pubs/761
