### PERBANDINGAN MODEL REGRESI KEGAGALAN PROPORSIONAL DARI COX MENGGUNAKAN METODE EFRON DAN EXACT

*Asri Lutfia Silmi  -  Departemen Statistika, Fakultas Sains dan Matematika, Universitas Diponegoro, Indonesia
Sudarno Sudarno  -  Departemen Statistika, Fakultas Sains dan Matematika, Universitas Diponegoro, Indonesia
Puspita Kartikasari  -  Departemen Statistika, Fakultas Sains dan Matematika, Universitas Diponegoro, Indonesia

Citation Format:
Abstract

Cox proportional hazard regression analysis is one of statistical methods that is often used in survival analysis to determine the effect of independent variables on the dependent variable in the form of survival time. Survival time starts from the beginning of the study until the event occurs or has reached the end of the study. The Cox proportional hazard regression model does not require information about the distribution that underlies the survival time but there is an assumption of proportional hazard that must be met. The purpose of this study is to determine the factors that influence the survival time of coronary heart disease. Ties are often found in survival data, including the survival data used in this study. Ties is an event when there are two or more individuals who experience a failure at the same time or have the same survival time value. The Efron and Exact method approach is used to overcome the presence of ties that can cause problems in the estimation of parameters associated with determining the members of the risk set. The results showed that the variables of diabetes mellitus, family history, and platelets significantly affected the survival time of CHD patients for both methods. The best model obtained is the Exact method because it has smaller AIC value of 383,153 compared to the AIC value of the Efron method of 393,207.

Keywords: Cox Proportional Hazard, Efron method, Exact method, Coronary heart disease

Article Metrics:

Article Info
Section: Articles
Language : ID
Recent articles
1. Bhandare, S. K. & Jain, Y. K., 2011. Min Max Normalization Based Data Perturbation Method for Privacy Protection. International Journal of Computer & communication Technology, 2(8), Hal. 45-50
2. Collet, D., 2003. Modelling Survival Data in Medical Research. 2nd ed. New York: CRC Press
3. Hosmer, D. W., Lemeshow, S. & May, S., 2008. Applied Survival Analysis. 2nd ed. New Jersey: Jhon Wiley
4. Kementrian Kesehatan Republik Indonesia. 2013. Riset Kesehatan Dasar 2013. Jakarta : Kementrian Kesehatan Republik Indonesia
5. Kementrian Kesehatan Republik Indonesia. 2014. Info DATIN Pusat Data dan Informasi Kementrian Kesehatan RI Situasi Kesehatan Jantung. Jakarta
6. Kleinbaum, D. G. & Klein, M., 2012. Survival Anyalysis A Self-Learning Text. 3rd ed. New York: Springer
7. Lee, E. T. & Wang, J. W., 2003. Statistical Methods for Survival Data Analysis. Third ed. Oklahoma: John Wiley & Sons, Inc.,Hoboken, New Jersey
8. Sari, D. M., A. & Purnakarya, I., 2010. Faktor Resiko Kolesterol Total Pasien Penyakit Jantung Koroner di Rumah Sakit Achmad Mochtar Bukittinggi. Jurnal Kesehatan Masyarakat, 4(2), Hal. 77-81

Last update:

No citation recorded.

Last update:

No citation recorded.