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Stat Med. 2011 May 30;30(12):1366-80. doi: 10.1002/sim.4205. Epub 2011 Feb 21.

A Bayesian semiparametric multivariate joint model for multiple longitudinal outcomes and a time-to-event.

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Department of Biostatistics, Erasmus Medical Center, P.O. Box 2040, 3000 CA Rotterdam, The Netherlands.


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.

[Indexed for MEDLINE]

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