Development of a surrogate endpoint for predicting graft failure in kidney transplant patientsGAŠPARAC, GRETA (Avtor) Štrumbelj, Erik (Mentor) Aggarwal, Varun (Komentor) kidney graft failure predictionmedicinesurvival analysisjoint modelingDue to the lack of available organs for transplantation it is important to make sure the kidney, once transplanted, survives. Researchers focus on the development of drugs that help prevent graft failure and throughout the process the support provided by statistical models can be beneficial. In this thesis we are given a dataset of patients from three clinics that have undergone kidney transplantation. Our main goal was to develop a model for graft failure prediction at different time points post-transplantation. We analyze the performance of different survival and machine learning models using simulated and real-life data and observe how they compare to the baseline model. Our results indicate that model performance depends on the amount of censored individuals and the amount of individuals that experience graft failure. We advocate for the use of simple models, since more complex approaches, such as joint modelling, often suffer from convergence problems, are more sensitive to model misspecification, and do not seem to add any additional value compared to other approaches. In the thesis we also provide a critical view of related work and review model evaluation metrics. We advocate for the use of inverse probability censoring weighted scoring rules and perform an experiment confirming that they provide unbiased estimates of the metric values under the assumption that censoring is non-informative.20222022-09-28 12:15:02Magistrsko delo/naloga141332sl