MODEL REGRESI COX PROPORTIONAL HAZARD PADA DATA KETAHANAN HIDUP PASIEN HEMODIALISA - Aprilia Sekar Khinanti; Sudarno Sudarno; Triastuti Wuryandari (Jurnal Gaussian, 2021)
Cox regression is a type of survival analysis that can be implemented with proportional hazard models or duration models. In the survival analysis data, there is a possibility that the data has ties, so it is necessary to use several approaches in estimating the parameters, namely the breslow, efron, and exact approaches. In this study, the Cox proportional hazard regression was used as a method of analysis for knowing the factors that influence the survival time on chronic kidney patients undergoing hemodialysis therapy. Based on the analysis that has been done, the best model is obtained with an exact approach and the factors that influence the survival time of hemodialysis patients are systolic blood pressure, hemoglobin level, and dialysis time. Hemodialysis patients who have high systolic blood pressure have a chance of failing to survive 12,950 times than normal systolic blood pressure.While the hemodialysis patient hemoglobin level increases, the hemodialysis patients chances of failing to survive is 0,6681 times less. Hemodialysis patients who received dialysis therapy with a dialysis time of more than four hours had 0.237 times the chance of failing to survive than patients with a dialysis time of less than or equal to 4 hours.Keywords: Cox Regression ,Survival, Ties, Hemodialysis.