DOI: https://doi.org/10.14710/j.gauss.12.3.453-464

PERBANDINGAN ANALISIS SURVIVAL MENGGUNAKAN REGRESI COX PROPORTIONAL HAZARD DAN REGRESI WEIBULL PADA PASIEN COVID-19 DI RSUD TAMAN HUSADA BONTANG

*Sindi Damayanti  -  Departemen Statistika, Fakultas Sains dan Matematika, Undip, Indonesia
Triastuti Wuryandari  -  Departemen Statistika, Fakultas Sains dan Matematika, Undip, Indonesia
Sudarno Sudarno  -  Departemen Statistika, Fakultas Sains dan Matematika, Undip, Indonesia
Open Access Copyright 2023 Jurnal Gaussian under http://creativecommons.org/licenses/by-nc-sa/4.0.

Citation Format:
Abstract

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.

Note: This article has supplementary file(s).

Fulltext View|Download |  Research Instrument
CTA Form
Subject
Type Research Instrument
  Download (595KB)    Indexing metadata
Keywords: COVID-19; Survival Analysis; Risk Factors; Cox Proportional Hazard; Weibull

Article Metrics:

Article Info
Section: Articles
Language : EN
Recent articles
PEMODELAN ANTAR VARIABEL EKONOMI SECARA SIMULTAN MENGGUNAKAN PENDEKATAN VECTOR ERROR CORRECTION MODEL (VECM) METODE TRIPLE EXPONENTIAL SMOOTHING HOLT-WINTER’S MULTIPLICATIVE DAN DEKOMPOSISI KLASIK MULTIPLIKATIF UNTUK PERAMALAN RATA-RATA KENAIKAN KONSENTRASI KARBON DIOKSIDA (CO2) GLOBAL KAJIAN SISTEM ANTRIAN PADA COUNTER KASIR DOMINO’S PIZZA MENGGUNAKAN MEAN VALUE ANALYSIS (STUDI KASUS: DOMINO’S PIZZA GAJAH MADA PEKALONGAN) More recent articles
  1. Chairina, P., Suyitno, & Siringoringo, M. 2020. Model-Model Regresi Weibull Univariat pada Indikator Pencemaran Air Dissolved Oxygen di Daerah Aliran Sungai Lingkungan Hutan Hujan Tropis Kalimantan Timur. Jurnal EKSPONENSIAL Vol. 11, No. 1, Hal: 19–28
  2. Chen, Y., Guo, Y., Pan, Y., & Zhao, Z. J. 2020. Structure Analysis of The Receptor Binding of 2019-nCoV. Biochemical and Biophysical Research Communications Vol. 525, No. 1, Hal: 135–140
  3. Collet, D. 2003. Modelling Survival Data in Medical Research Second Edition. London: Chapman and Hall
  4. Fernandes, A. A. R., & Solimun. 2016. PEMODELAN STATISTIKA PADA ANALISIS RELIABILITAS DAN SURVIVAL. Malang: UB Media
  5. Guan, W., Ni, Z., Hu, Y., Liang, W., Ou, C., He, J., Liu, L., Shan, H., Lei, C., Hui, D. S. C., Bin Du, Li, L., Zeng, G., Yuen, K.-Y., Chen, R., Tang, C., Wang, T., Chen, P., Xiang, J., … Zhong, N. 2020. Clinical Characteristics of Coronavirus Disease 2019 in China. New England Journal of Medicine Vol. 382, No. 18, Hal: 1708–1720
  6. Jenkins, Stephen P. 2004. Survival Analysis. Unpublished manuscript, In-
  7. stitute for Social and Economic Research, University of Essex, Colchester, UK
  8. Kleinbaum, D. G., & Klein, M. 2012. Survival Analysis: A Self-Learning Text 3rd Edition. New York. Springer-Verlag
  9. Kurnia, D., & Effendi, R. 2021. Inflamasi pada Coronavirus Disease 2019. Baiturrahmah Medical Journal Vol. 1, No. 1, Hal: 77–86
  10. Lawless, J. F. 1982. Statistical Models and Methods for Lifetime Data. Hoboken. John Wiley & Sons, Inc
  11. Lee, E. T., & Wang, J. W. 2003. Statistical Methods for Survival Data Analysis. Hoboken. John Wiley & Sons, Inc
  12. Schoenfeld, D. 1982. Partial Residuals for The Proportional Hazards Regression Model. Biometrika Vol. 69, No. 1, Hal: 239–241
  13. Sulantari & Hariadi, W. 2022. Analisis Survival Model Regresi Cox Pada Lama Waktu Sembuh Pasien Gejala Sedang Covid-19. Jurnal UJMC Vol. 8, No.1, Hal: 43–54
  14. Susilo, A., Rumende, C. M., Pitoyo, C. W., Santoso, W. D., Yulianti, M., Herikurniawan, Sinto, R., Singh, G., Nainggolan, L., J. Nelwan, E., Chen, L. K., Widhani, A., Wijaya, E., Wicaksana, B., Maksum, M., Annisa, F., Jasirwan, C. O., & Yunihastuti, E. 2020. Coronavirus disease 2019: Tinjauan Literatur Terkini. Jurnal Penyakit Dalam Indonesia Vol. 7, No. 1, Hal: 45–67
  15. Wu, Z., & McGoogan, J. M. 2020. Characteristics of and Important Lessons From The Coronavirus Disease 2019 (COVID-19) Outbreak in China: Summary of a Report of 72.314 Cases From The Chinese Center for Disease Control and Prevention. Jama Vol. 323, No. 13, Hal: 1239–1242

Last update:

No citation recorded.

Last update:

No citation recorded.