ANALISIS SURVIVAL DENGAN COX PROPORTIONAL HAZARD PADA KASUS DEMAM TIFOID

Abstract

A common problem found in survival data is the presence of censored data. The length of hospitalization of Typhoid fever patients until declared cured is one of example of this data.  Here, we use Cox regression model to analysis this data.  Partial likelihood is one of the methods of estimating parameters for Cox regression model.  In many cases of censored data, two objects (patients) have the same length of hospitalization (ties). Therefore, to estimate the parameters of the model must use the right method. Here we used partial likelihood Breslow, Efron, and Exact methods. The study was motivated by how the three methods performed for Cox regression model. The data used for the implementation of these methods is length of hospitalization of Typhoid fever patients at Mekar Sari Hospital-Bekasi in 2020.  Based on AIC criteria, we found that exact method is the best model (minimum AIC) for parameter estimation of Cox regression model. Referring to the Cox regression model by using a significance level of 10%, there are five predictor variables that affects the length of patient hospitalization. The five variables are age, vomiting, dirty tongue, hemoglobin, and leukocyte.
Keywords: Typhoid fever, Cox regression, Breslow method, Efron method, exact method.
MSC2020: 62N02, 62N03