PERBANDINGAN ANALISIS SURVIVAL MENGGUNAKAN REGRESI COX PROPORTIONAL HAZARD DAN REGRESI WEIBULL PADA PASIEN COVID-19 DI RSUD TAMAN HUSADA BONTANG - Damayanti, Sindi; Wuryandari, Triastuti; Sudarno, Sudarno (Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro, 2023)
COVID-19 is brought on by the Severe Acute Respiratory Syndrome Coronavirus-2 (SARS-CoV-2) and transmitted to humans through animal. SARS-CoV-2 infection affects patient's metabolism and causes hyperinflammatory. This condition affects individuals with risk factors such as age, gender, diabetes, heart disease, hypertension, Chronic Obstructive Pulmonary Disease (COPD), obesity, and Acute Respiratory Distress Syndrome (ARDS). One approach to figuring out the association between the time of an occurrence and the independent factors is the Cox Proportional Hazard Regression. The Cox PH regression is a semiparametric model because it doesn’t require a specific distribution test. There is a parametric model used in modeling and analyzing failure time data, namely Weibull regression. The case study is patients with COVID-19 at Taman Husada Bontang Regional Public Hospital who underwent hospitalization from August 2021 to September 2021 data. Based on the Cox PH Regression and Weibull Regression models, variables that affect the survival time of COVID-19 patients are heart disease and ARDS. The AIC value obtained using the Cox Proportional Hazard regression is 635.6149, this value is smaller than the Weibull regression which is 745.5509 so the use of survival analysis with the Cox Proportional Hazard regression is better than the Weibull regression in this case.