Joint Models for longitudinal and survival data are well established statistical models in the context of biostatistics. The need to account for non missing at random repeated measurements in longitudinal studies is the motivation for these models. Under classical statistical inference, using maximum likelihood function, it is necessary to integrate out a high dimension vector of random effects, which makes these models computationally difficult to implement. An alternative approach is to use Bayesian inference, sampling from the posterior distribution using MCMC techniques. However, Rue et al (2009) propose an Integrated Nested Laplace Approximation approach to sample from the posterior distribution. This method makes in fact the computation much faster and of easy implementation. In this work we propose to fit joint models under this approach in a Bayesian inference setting. |