Recently, there has been an increasing interest in using longitudinal biomarkers to characterize the occurrence of an event, such as death. In this context, two outcomes from the same subject are simultaneously observed: repeated measures and time-to-event. The inherent association between them has brought the joint modelling framework. Furthermore, there is a growing priority on placing patients at the centre of healthcare research. In this context, patient-reported outcomes (PROs) are helpful tools for informing clinicians about patients’ health status and quality of life. We propose a joint modelling Bayesian approach for longitudinal PRO measurements and survival data that includes adequate distributional fits of PRO by considering its nature and characteristics.