COX PROPORTIONAL HAZARD REGRESSION SURVIVAL ANALYSIS FOR TYPE 2 DIABETES MELITUS

  • Umi Mahmudah Tadris Mathematics Study Program, Faculty of Tarbiyah and Educational Sciences IAIN Pekalongan
  • Sugiyarto Surono Mathematics, Faculty of Applied Science and Technology, Ahmad Dahlan University
  • Puguh Wahyu Prasetyo Mathematics Education, Faculty of Teacher Training and Education, Ahmad Dahlan University
  • Muhamad Safiih Lola Mathematics sciences, Faculty of Ocean Engineering Technology and informatics University Malaysia Trengganu
  • Annisa Eka Haryati Mathematics Education, Faculty of Teacher Training and Education, Ahmad Dahlan University
DOI: https://doi.org/10.30598/barekengvol16iss1pp251-260
Keywords: Cox regression, Diabetes melitus, Hazard ratio, Proportional hazard, Survival analysis

Abstract

One of the most widely used methods of survival analysis is Cox proportional hazard regression. It is a semiparametric regression used to investigate the effects of a number of variables on the dependent variable based on survival time. Using the Cox proportional hazard regression method, this study aims to estimate the factors that influence the survival of patients with type 2 diabetes mellitus. The estimated parameter values, as well as the Cox Regression equation model, were also investigated. A total of 1293 diabetic patients with type 2 diabetes were studied, with data taken from medical records at PKU Muhammadiyah Hospital in Yogyakarta, Indonesia. These variables have regression coefficients of 1.36, 1.59, -0.63, 0.11, and 0.51, respectively. Furthermore, the results showed the hazard ratio for female patients was 1.16 times male patients. Patients on insulin treatment had a 4.92-fold higher risk of death than those on other therapy profiles. Patients with normal blood sugar levels (GDS 140 mg/dl) had a 1.12 times higher risk of death than those with other blood glucose levels. Type 2 diabetes mellitus is a challenge for many Indonesians, in addition to being a deadly condition that was initially difficult to diagnose. As a result, patient survival analysis is needed to reduce the patient's risk of death.

Downloads

Download data is not yet available.

References

D. Collett, Modelling survival data in medical research. CRC press, 2015.

D. E. McGregor, J. Palarea-Albaladejo, P. M. Dall, K. Hron, and S. F. M. Chastin, “Cox regression survival analysis with compositional covariates: Application to modelling mortality risk from 24-h physical activity patterns,” Stat. Methods Med. Res., vol. 29, no. 5, pp. 1447–1465, 2020, doi: 10.1177/0962280219864125.

N. Benítez-Parejo, M. M. Rodríguez del Águila, and S. Pérez-Vicente, “Survival analysis and Cox regression,” Allergol. Immunopathol. (Madr)., vol. 39, no. 6, pp. 362–373, 2011, doi: 10.1016/j.aller.2011.07.007.

A. Y. Dewi, N. K. Dwidayati, and A. Agoestanto, “Analisis Survival Model Regresi Cox Dengan Metode MLE Untuk Penderita Diabetes Mellitus,” Unnes J. Math., vol. 9, no. 1, pp. 31–40, 2020.

S. Annibali, N. Pranno, M. P. Cristalli, G. La Monaca, and A. Polimeni, “Survival Analysis of Implant in Patients with Diabetes Mellitus: A Systematic Review,” Implant Dent., vol. 25, no. 5, pp. 663–674, 2016, doi: 10.1097/ID.0000000000000478.

M. Marchetti et al., “Second cancers in MPN: Survival analysis from an international study,” Am. J. Hematol., vol. 95, no. 3, pp. 295–301, 2020.

E. Murillo-Zamora, X. Trujillo, M. Huerta, M. Ríos-Silva, and O. Mendoza-Cano, “Male gender and kidney illness are associated with an increased risk of severe laboratory-confirmed coronavirus disease,” BMC Infect. Dis., vol. 20, no. 1, pp. 1–8, 2020.

Á. Nagy, G. Munkácsy, and B. Győrffy, “Pancancer survival analysis of cancer hallmark genes,” Sci. Rep., vol. 11, no. 1, pp. 1–10, 2021.

H. Sato et al., “Prognostic significance of skeletal muscle decrease in unresectable pancreatic cancer: Survival analysis using the Weibull exponential distribution model,” Pancreatology, 2021.

C. X. Wang, I. Pusic, and M. J. Anadkat, “Association of leukemia cutis with survival in acute myeloid leukemia,” JAMA dermatology, vol. 155, no. 7, pp. 826–832, 2019.

M. Panahiazar, V. Taslimitehrani, N. Pereira, and J. Pathak, “Using EHRs and machine learning for heart failure survival analysis,” Stud. Health Technol. Inform., vol. 216, p. 40, 2015.

B. Matti and K. Zargar-Shoshtari, “Prostate cancer outcomes disparities: Population survival analysis in an ethnically diverse nation,” 2021.

A. Anisa, “Analisis Status Kelangsungan Hidup Penderita Penyakit Diabetes Mellitus (DM) RS. Wahidin Sudirohusodo Makassar Dengan Model Regresi Logistik Nominal,” J. Mat. Stat. dan Komputasi, vol. 14, no. 1, pp. 37–45, 2018.

H.-J. Anders, T. B. Huber, B. Isermann, and M. Schiffer, “CKD in diabetes: diabetic kidney disease versus nondiabetic kidney disease,” Nat. Rev. Nephrol., vol. 14, no. 6, pp. 361–377, 2018.

D. Prasetyani and S. Sodikin, “Analisis Faktor Yang Mempengaruhi Kejadian Dm Melitus (Dm) Tipe 2,” J. Kesehat. Al-Irsyad, pp. 1–9, 2017.

E. K. Tannor, F. S. Sarfo, L. M. Mobula, O. Sarfo‐Kantanka, R. Adu‐Gyamfi, and J. Plange‐Rhule, “Prevalence and predictors of chronic kidney disease among Ghanaian patients with hypertension and diabetes mellitus: A multicenter cross‐sectional study,” J. Clin. Hypertens., vol. 21, no. 10, pp. 1542–1550, 2019.

W. Meidikayanti and C. U. Wahyuni, “Hubungan dukungan keluarga dengan kualitas hidup Diabetes melitus tipe 2 di puskesmas pademawu,” J. Berk. Epidemiol., vol. 5, no. 2, pp. 240–252, 2017.

N. Ramadhan, N. Marissa, E. Fitria, and V. Wilya, “Pengendalian diabetes melitus tipe 2 pada pasien di Puskesmas Jayabaru Kota Banda Aceh,” Media Penelit. dan Pengemb. Kesehat., vol. 28, no. 4, pp. 239–246, 2018.

L. P. Hutahaean, M. A. Mukid, and T. Wuryandari, “Model Regresi Cox Proportional Hazards Pada Data Lama Studi Mahasiswa (Studi Kasus Di Fakultas Sains dan Matematika Universitas Diponegoro Semarang Mahasiswa Angkatan 2009),” J. Gaussian, vol. 3, no. 2, pp. 173–181, 2014.

A. Andriyati and E. Rohaeti, “Analisis Faktor-Faktor yang Mempengaruhi Ketahanan Hidup Pasien Tuberculosis dengan Model Regresi Cox (Studi kasus: Rumah Sakit Paru Bogor),” Kubik J. Publ. Ilm. Mat., vol. 4, no. 1, pp. 140–148, 2019.

S. Han et al., “Cox regression model of prognostic factors for delayed neuropsychiatric sequelae in patients with acute carbon monoxide poisoning: A prospective observational study,” Neurotoxicology, vol. 82, pp. 63–68, 2021.

W. Sanusi, A. Alimuddin, and S. Sukmawati, “Model Regresi Cox dan Aplikasinya dalam Menganalisis Ketahanan Hidup Pasien Penderita Diabetes Mellitus di Rumah Sakit Bhayangkara Makassar,” J. Math. Comput. Stat., vol. 1, no. 1, pp. 62–77, 2019.

T. Vivekanandan and S. J. Narayanan, “A Hybrid Risk Assessment Model for Cardiovascular Disease Using Cox Regression Analysis and a 2-means clustering algorithm,” Comput. Biol. Med., vol. 113, p. 103400, 2019.

N. Ata and M. T. Sözer, “Cox Regression Models with Nonproportional Hazards Applied to Lung Cancer Survival Data,” Hacettepe J. Math. Stat., vol. 36, no. 2, pp. 157–167, 2007.

D. . Kleinbaum and M. Klein, Survival Analysis. New York: Springer New York LLC, 2011.

P. D. Allison, Survival analysis using SAS: a practical guide. Sas Institute, 2010.

Published
2022-03-21
How to Cite
[1]
U. Mahmudah, S. Surono, P. Prasetyo, M. Lola, and A. Haryati, “COX PROPORTIONAL HAZARD REGRESSION SURVIVAL ANALYSIS FOR TYPE 2 DIABETES MELITUS”, BAREKENG: J. Math. & App., vol. 16, no. 1, pp. 253-262, Mar. 2022.
Issue
Vol 16 No 1 (2022): BAREKENG: Jurnal Ilmu Matematika dan Terapan
Section
Articles

Authors who publish with this Journal agree to the following terms:

  1. Author retain copyright and grant the journal right of first publication with the work simultaneously licensed under a creative commons attribution license that allow others to share the work within an acknowledgement of the work’s authorship and initial publication of this journal.
  2. Authors are able to enter into separate, additional contractual arrangement for the non-exclusive distribution of the journal’s published version of the work (e.g. acknowledgement of its initial publication in this journal).
  3. Authors are permitted and encouraged to post their work online (e.g. in institutional repositories or on their websites) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published works.