Published Paper Details:

A major global public health issue linked to increased morbidity and mortality is Chronic Kidney Disease (CKD), a continuum of kidney disease ranging from minor kidney impairment to End-Stage Renal Disease (ESRD). For ESRD patients, longevity and quality of life have improved thanks to advancements in dialysis treatments and the availability of kidney transplantation. In survival analysis, survival data might include patient characteristics that are connected to response, survival, and the development of a disease, as well as survival time and response to a particular treatment. Comparing the survival distributions of experimental animals or human patients, predicting the likelihood of response, survival, or mean lifespan, and identifying risk and/or prognostic factors related to response, survival, and the onset of a disease have been the main goals of the study of survival data. In the modelling of survival data under various types of diseases, parametric regression models are frequently utilised. This study uses the CKD data to examine the effectiveness of the four most used parametric regression modelling techniques: Exponential, Weibull, Gamma, and Lognormal. Based on the results and the minimum values of AIC and BIC, it is concluded that the Gamma model performs better than other models.