Getachew Beyene2025-06-212023-11-28https://repository.mu.edu.et/handle/123456789/67010.82589/muir-585Background: Chronic Kidney Disease (CKD) or renal failure is a public global health problem with an estimated prevalence of as 8 to 16% worldwide. This study was conducted inorder to investigate the evolution of hematocrit levels over time in renal patients after their transplant and to determine how the evolution depends on the age and gender of the patient and other factors. Objective: The main objective of this study is to employ a mixed effect model to examine the unbalanced longitudinal evolution progress of hematocrit levels in chronic kidney failure patients. Methodology: This is a longitudinal study that consisted of 1160 patients who received a renal transplant. These patients were followed up for a period of 10 years at most. Haematocrit level was considered as the response while the covariates were time in years, gender and age of the patients just to mention a few. Different statistical methods such as explanatory analysis, multivariate regression model, two stage analysis and linear mixed effects model were employed to explore the evolution of hematocrit over time. Results: Results revealed that haematocrit levels in kidney transplantpatients evolve over time. Gender and age of the patient have significant effect on the evolution of haemotocrit levels. Males tend to have a higher increase in haematocrit levels over time than females. With regard to age, haematocrit levels tend to increase with increasing age. Furthermore, it was observed that experience of cardio-vascular problems before transplant and rejection symptoms did not have a significant effect on the evolutionof haematocrit levels. Conclusions: Hematocrit levels evolve over time and this evolution follows a quartic time effect. The change in haematocrit levels varies according to the gender and age of the patient after a kidney transplant. Patients starting with low haematocrit levels tend to have a larger increase overtime.enkidney transplantlongitudinal datamultivariate modeltwo-stage analysisand linear mixed effects modelThesis