ANALISIS SURVIVAL COX STRATIFIKASI PENDEKATAN BAYESIAN PADA PENYAKIT GINJAL KRONIK DENGAN HEMODIALISIS - SUHARTATIK, 101614153019 (2018)
Cox regression is a widely used in the survival analysis which has terms proportional hazards assumption. The stratified cox regression is a modification of cox proportional hazard that controls independent variables with considerate the confounder. The stratified cox regression can be used in the analysis of time-toevent data for hemodialysis patients. Hemodialysis is an alternative method to prolong the life of chronic kidney patients. The aim of this study was survival analysis in the patients of chronic kidney diseases and got the best model of Bayesian stratified cox model. This study was observational analytic with retrospective cohort design. The population of this study was all hemodialysis patients in 2013-2014 at RSU Haji Surabaya. Sampling consisted 120 patients. Analysis data used R program. Survival analysis using the cox proportional hazards model showed that underlying disease (X4), comorbidities (X6) and the adequacy of dialysis (X5) were significantly associated with survival of hemodialysis patients. The estimation of cox proportional hazards model was: ( ) ( ) ( The best model was Bayesian proportional hazard cox model (DIC=601.2105). Hence, it can be concluded that survival of hemodialysis patient was influenced by underlying disease, comorbidities and the adequacy of dialysis. In addition, the best method was Bayesian proportional hazard cox model. It is suggested to Health Service Centre to improving quality of service related adequacy of dialysis variable affected survival hemodialysis patients and requires further analysis of the factors that influence adequacy of dialysis.