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Dynamic predictions of survival outcomes are of great interest to physiciansand patients, since such predictions are useful elements of clinicaldecision-making. Joint modelling of longitudinal and survival data has beenincreasingly used to obtain dynamic predictions. A common assumption of jointmodelling is that random-effects and error terms in the longitudinal sub-modelare Gaussian. However, this assumption may be too restrictive, e.g. in thepresence of outliers as commonly encountered in many real-life applications. Anatural extension is to robustify the joint models by assuming more flexibledistributions than Gaussian for the random-effects and/or error terms. Previousresearch reported improved performance of robust joint models compared to theGaussian version in terms of parameter estimation, but dynamic predictionaccuracy obtained from such approach has not been yet evaluated. In this study,we define a general robust joint model with t-distributed random-effects anderror terms under a Bayesian paradigm. Dynamic predictions of graft failurewere obtained for kidney transplant recipients from the French transplantcohort, DIVAT. Calibration and discrimination performances of Gaussian androbust joint models were compared for a validation sample. Dynamic predictionsfor two individuals are presented.

Dynamic predictions of kidney graft survival in the presence of longitudinal outliers