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Abstract Haematocrit Levels is defined as the proportion, by volume, of the blood that consists of red blood cells. This study was conducted to model the progress of haematocrit levels over time in kidney patients after their transplant and to determine how the progress depends on the age and gender of the patient and other factors. This is a longitudinal study, and haematocrit level was considered as a response while the covariates were time, gender, age, cardio-vascular problem during the years preceding the transplantation, and rejection symptoms during the first three months after the transplantation took place of the patients. Different statistical methods such as, multivariate regression model, two stage analysis and linear mixed effects model were employed to explore the progress of haematocrit over time. Models revealed that haematocrit levels in kidney transplant patients evolve over time. Gender and Age of the patient have significant effect on the evolution of haematocrit levels. Males tend to have a high increase in haematocrit levels over time than females. With regards to age, haematocrit levels tend to increase with increasing age. Moreover, it was observed that experience of cardio-vascular problems before trandplant and rejection symptoms did not have a significant effect on the progress of haematicrit levels. The growth of Haematocrit levels evolve over time follows a quartic time effect. Change in haematocrit levels varies according to gender and age of the patient. Patients starting with low haematocrit levels tend to have a larger increase over time.

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 in order to Specify Dropout Models the haematocrit levels over time in renal patients after their transplant. This is a longitudinal study that consisted of 1160 patients who received a renal transplant. Level of Haematocrit level as a response, time (log (years)), gender and age of the patients just to mention a few were as a covariate. Different statistical methods, such as dropout models were employed. Direct likelihood and MI based approach of Linear Mixed Model revealed that all the covariates included in the final models were significant. It was observed that the evolution of haematocrit levels follows a quartic time trend and the effect of time on the evolution varies according to gender and age. The effect of time followed a significant 5 th power time trend under MI GEE and quadratic time trend under WGEE. However, both models are valid under MAR. In addition, age, gender and rejection symptoms have a significant effect on the evolution of haematocrit levels in renal patients, however their effect varies with time.