Summary. Patients undergoing renal transplantation are prone to graft failure which causes loss of follow-up measures on their levels of blood urea nitrogen and serum creatinine. These two outcomes are measured repeatedly over time to assess renal function following transplantation. Loss of follow-up on these bivariate measures results in informative right censoring, which is a common problem in longitudinal data that should be adjusted for so that valid estimates are obtained. In this study, we propose a bivariate model that jointly models these two longitudinal correlated outcomes and generates population and individual slopes adjusting for informative right censoring by using a discrete survival approach. The approach proposed is applied to a clinical data set of patients who had undergone renal transplantation. A simulation study validates the effectiveness of the approach.