skip to main content

PERBANDINGAN MODEL REGRESI STRATIFIED COX DAN EXTENDED COX PADA ANALISIS SURVIVAL PENDERITA KANKER PAYUDARA

*Jessika Aurora Samosir  -  Departemen Statistika, Fakultas Sains dan Matematika, Universitas Diponegoro, Indonesia
sudarno sudarno  -  Departemen Statistika, Fakultas Sains dan Matematika, Universitas Diponegoro, Indonesia
Di Asih I Maruddani  -  Departemen Statistika, Fakultas Sains dan Matematika, Universitas Diponegoro, Indonesia
Open Access Copyright 2024 Jurnal Gaussian under http://creativecommons.org/licenses/by-nc-sa/4.0.

Citation Format:
Abstract
Breast cancer is a tumor that develops in the breast where cells in the mammary gland divide and develop uncontrollably. Breast cancer is the most common cancer that causes death in women among other cancers. This study aims to determine the factors that affect the survival of breast cancer patients from the METABRIC database. This study was analyzed using survival analysis method. The method that is often used is the Cox proportional hazard model where the proportional hazard assumption must be met. There is variable that do not meet the assumption so that the methods used are stratified Cox and extended Cox. The stratified Cox model overcomes variables that do not meet the assumption by stratifying variables that do not meet the assumption. The extended Cox model overcomes variables that do not meet the assumption by interacting the variables with a time function. The time functions used in this study are linear time functions and logarithmic time functions. Based on the smallest AIC value, the best model is the stratified Cox regression model without interaction. Factors that affect the survival of breast cancer patients from the METABRIC database are tumor size, chemotherapy, stage 1, stage 2, and type of surgery.

Note: This article has supplementary file(s).

Fulltext View|Download |  Research Instrument
CTA Form
Subject
Type Research Instrument
  Download (223KB)    Indexing metadata
Keywords: Breast cancer; stratified Cox; extended Cox; METABRIC.

Article Metrics:

  1. Aini, I.N., (2011). “Extended Cox Model untuk Time-Independent Covariate Yang Tidak Memenuhi Asumsi Proportional Hazard Pada Model Cox Proportional Hazard”. Depok : Universitas Indonesia
  2. Bray, F., Ferlay, J., Soerjomataram, I., Siegel, R. L., Torre, L.A., & Jemal, A. (2018). Global Cancer Statistics 2018: GLOBOCAN Estimates of Incidence and Mortality Worldwide for 36 Cancers in 185 Countries. CA: A Cancer Journal for Clinicians. 68(6). 394-424
  3. Coleman, M.P., et al. (2008) Cancer Survival in Five Continents: A Worldwide Population-Based Study (CONCORD). Lancet Oncology, 9, 730-756
  4. Collet, D. (2003). Modelling Survival Data in Medical Research Second Edition. Chapman & Hall
  5. Cox, D.R., & Oakes, D. (1984). Analysis of Survival Data. London : Chapman and Hall
  6. Hosmer, W. D., & Lemeshow, S. (1997). Applied Survival Analysis Regression Modelling of Time to Event Data. New York : Wiley
  7. Klein, J.P., & Moeschberger, M.L. (2003). Survival Analysis Techniques for Censored and Truncated Data Second Edition . Berlin : Springer
  8. Kleinbaum, D.G., & Klein, M. (2012). Survival Analysis A Self-Learning Text Third Edition. New York : Springer
  9. Lee, E.T., & Wang, J.W. (2003). Statistical Methods for Survival Data Analysis. New York : Wiley
  10. Maulida, E.A., (2019). “Analisis Ketahanan Hidup Penderita Kanker Payudara Menggunakan Regresi Cox Proportional Hazard dan Metode Kaplan Meier”. Skripsi. Surabaya : UIN Sunan Ampel
  11. Nurfain, Purnami, S.W. (2017). Analisis Regresi Cox Extended pada Penderita Kusta di Kecamatan Brondong Kabupaten Lamongan. Jurnal Sains dan Seni ITS, 6(1), 89-93
  12. Pahlevi, M.R., Mustafid, & Wuryandari, T. (2016). Model Regresi Cox Stratified Pada Data Ketahanan. Jurnal Gaussian, 5(3), 455-464
  13. Scrowcroft, H., (2012). Increasing the resolutionon breast cancer – the METABRIC study. https://news.cancerresearchuk.org/2012/04/18/increasing-the-resolution-on-breast-cancer-the-metabric-study/. Diakses : 14 Februari 2023
  14. Sinaga, E. S., Ahmad, R.A., & Hutajulu, S.H. (2017). Analisis Ketahanan Hidup 5 Tahun pada Penderita Kanker Payudara di RS Sardjito Provinsi Yogyakarta Indonesia. Berita Kedokteran Rakyat, 33(2), 67-72
  15. Sung, H., Ferlay, J., Siegel, R.L., Laversanne, M., Soerjomataram, I., Jemal, A., & Bray, F. (2020). Global Cancer Statistics 2020: GLOBOCAN Estimates of Incidence and Mortality Worldwide for 36 Cancers in 185 Countries. CA: A Cancer Journal for Clinicians. 71(3). 209-249
  16. Torre, L.A., Bray, F., Siegel, R.L., Ferlay, J., Lortet-Tieulent, J., & Jemal, A. (2012). Global Cancer Statistics 2012. CA: A Cancer Journal for Clinicians. 65(2). 87-108

Last update:

No citation recorded.

Last update:

No citation recorded.