A Bayesian semiparametric multivariate joint model for multiple longitudinal outcomes and a time-to-event
Article first published online: 21 FEB 2011
Copyright © 2011 John Wiley & Sons, Ltd.
Statistics in Medicine
Volume 30, Issue 12, pages 1366–1380, 30 May 2011
How to Cite
Rizopoulos, D. and Ghosh, P. (2011), A Bayesian semiparametric multivariate joint model for multiple longitudinal outcomes and a time-to-event. Statist. Med., 30: 1366–1380. doi: 10.1002/sim.4205
- Issue published online: 10 MAY 2011
- Article first published online: 21 FEB 2011
- Manuscript Accepted: 7 JAN 2011
- Manuscript Received: 3 MAR 2010
- Dirichlet process prior;
- shared parameter model;
- survival analysis;
- time-dependent covariates
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.