Analysis of the Death Risk of Covid-19 Patients Using Extended Cox model

  • Cyndy Romarizka Jurusan Matematika, Fakultas Matematika dan Ilmu Pengetahuan Alam, Universitas Jember
  • Mohamat Fatekurohman Jurusan Matematika, Fakultas Matematika dan Ilmu Pengetahuan Alam, Universitas Jember
  • I Made Tirta Jurusan Matematika, Fakultas Matematika dan Ilmu Pengetahuan Alam, Universitas Jember


Globally, in 2021, there were 170,051,718 COVID-19 cases and 3,540,437 patients who died. The high mortality rate of patients infected with COVID-19 gives an idea to research the analysis of the factors that influence the death of Covid-19 patients. The data used in this study is data on Covid-19 patients obtained from the Mexican Government, with response variables namely time and status and predictor variables, namely patient laboratory results in the form of a history of illness that has been suffered by Covid-19 patients so that they adopt the extended model to evaluate the data. The data in this study are heterogeneous and large in number so that data clustering is carried out into 3 clusters, namely low emergency clusters, medium emergency clusters and high emergency clusters using K-means clustering. Because the study could not find the factors that influence the death of Covid-19 patients, two clusters were chosen, namely the medium emergency cluster and the high emergency cluster. So that the factors that influence the death of Covid-19 patients in the medium emergency cluster are sorted by the highest hazard ratio, namely pneumonia, old age, renal chronic, diabetes, Chronic Obstructive Pulmonary Disease (COPD), immune system, hypertension, cardiovascular, obesity, gender, and asthma. In the high emergency cluster, sorted by the highest hazard ratio is the immune system, renal chronic, cardiovascular, COPD, tobacco, hypertension, obesity, gender, and pneumonia.


Aini IN. 2011. Extended cox Model Time for Time Independent Covariates That Violate the Proportional hazard Assumption in Cox Propotional Hazard Model. [Thesis] Fakultas Matematika dan Ilmu Pengetahuan Alam Universitas Indonesia, Depok

Audina B & Fatekurohman M. 2020. Analisis Survival pada Data Pasien Covid 19 di Kabupaten Jember. BERKALA SAINSTEK. 8(4): 118-121

Backer JA, Don K & Jacco W. 2020. Incubation period of 2019 novel coronavirus (2019-nCoV) infections among travellers from Wuhan, China, 20-28 January 2020. Euro surveill. 25(5): 1-6.

Creed JH, Gerke TA & Berglund AE. 2020. Matsurv: Survival Analysis and Visualization In MATLAB. The Journal of Open Source Software. 5(46): 1-5.

Dai NF. 2020. Stigma Masyarakat Terhadap Pandemi Covid-19. Prosiding Seminar nasional Problematika Sosial Pandemi Covid-19. Indonesia. 66-73.

Ejaz H, Alsrhani A, Zafar A, Javed H, Junaid K, Abdalla AE, Abosalif KO, Ahmed Z & Younas S. 2020. COVID-19 and comorbidities: Deleterious impact on infected patients. Journal of Infection and Public Health. 13(12): 1833-1839.

Hafid H, Bustan MN & Aidid MK. 2020. Penanganan Ties Event dalam Regresi Cox Proportional Hazard Menggunakan Metode Breslow (Kasus: Pasien Rawat Inap DBD di RSAL Jala Ammari Makassar). Journal of Statistics and Its Application on Teaching and Research. 5(2): 13-19.

Hanifa N. 2021. Pemodelan Regresi Cox Proportional hazard Pada Ketahanan Hidup Pasien COVID-19 dengan Gejala Berat di Rumah sakit Universitas Airlangga Surabaya. [Thesis] Institut Teknologi Sepuluh November, Surabaya.

Kurniawan I, Kurnia A, & Sartono B. 2015. Survival Analysis with Extended Cox Model About Durability Debtor Efforts on Credit Risk. Indonesian Journal of Statistics. 20(2): 85-95.

Lu Q & Shi Y. 2020. Coronavirus disease (COVIDÔÇÉ19) and neonate: What neonatologist need to know. Medical Virology. 92(6): 1-4.

Rahayu LAD, Admiyanti JC, Khalda YI, Ahda FR, Agistany NFF, Setiawati S, Shofiyanti NI & Warnaini C. 2021. Hipertensi, Diabetes Melitus, dan Obesitas Sebagai Faktor Komorbiditas Utama Terhadap Mortalitas Pasien Covid-19: Sebuah Studi Literatur. Jurnal Ilmiah Mahasiswa Kedokteran Indonesia. 9(1): 91-97.

Riyandianci N. 2017. Analisis Survival Pada Pasien Penderita Kanker Serviks di RSUD dr. Soetomo Surabaya Menggunakan Stratified Cox dan Extended cox. [Thesis] Institut Teknologi Sepuluh November, Surabaya.

Rulli E, Ghilotti F, Biagioli E, Porcu L, Marabese M, D'Incalci M, Bellocco R & Torri V. 2018. Assessment of Proportional Hazard Assumption in Aggregate Data: A Systematic Review on Statistical Methodology In Clinical Trials Using Time-To-Event Endpoint. British Journal of Center. 119: 1456-1463.

Sulantari & Wigid H. 2020. Analisis Survival Waktu Sembuh Pasien Covid-19 di Kabupaten Banyuwangi. Jurnal Pendidikan Matematika dan Matematika. 4(2): 375-386.

Zhou F, Yu T, Du R, Fan G, Liu Y, Liu Z, Xiang J, Wang Y, Song B, Gu X, Guan L, Wei Y, Li H, Wu X, Xu J, Tu S, Zhang Y, Chen H & Cao B. 2020. Clinical Course and Risk Factors for Mortality Of Adult Inpatients With COVID-19 In Wuhan, China: A Retrospective Cohort Study. The Lancet. 395(10229): 1054-1062.
How to Cite
ROMARIZKA, Cyndy; FATEKUROHMAN, Mohamat; TIRTA, I Made. Analysis of the Death Risk of Covid-19 Patients Using Extended Cox model. Jurnal ILMU DASAR, [S.l.], v. 24, n. 1, p. 65-74, jan. 2023. ISSN 2442-5613. Available at: <>. Date accessed: 09 feb. 2023. doi: