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