  Dynamic predictions of survival outcomes are of great interest to physicians
and patients, since such predictions are useful elements of clinical
decision-making. Joint modelling of longitudinal and survival data has been
increasingly used to obtain dynamic predictions. A common assumption of joint
modelling is that random-effects and error terms in the longitudinal sub-model
are Gaussian. However, this assumption may be too restrictive, e.g. in the
presence of outliers as commonly encountered in many real-life applications. A
natural extension is to robustify the joint models by assuming more flexible
distributions than Gaussian for the random-effects and/or error terms. Previous
research reported improved performance of robust joint models compared to the
Gaussian version in terms of parameter estimation, but dynamic prediction
accuracy obtained from such approach has not been yet evaluated. In this study,
we define a general robust joint model with t-distributed random-effects and
error terms under a Bayesian paradigm. Dynamic predictions of graft failure
were obtained for kidney transplant recipients from the French transplant
cohort, DIVAT. Calibration and discrimination performances of Gaussian and
robust joint models were compared for a validation sample. Dynamic predictions
for two individuals are presented.
