Analisis Faktor yang Berpengaruh terhadap Waktu Survival Pasien Penyakit Ginjal Kronis menggunakan Uji Asumsi Proportional Hazard

Assyifa Lala Pratiwi Hamid, Sri Subanti, Yuliana Susanti

Abstract

Chronic kidney disease is a disease whose risk of death is always increasing. This disease was ranked as the 13th leading cause of death in Indonesia in 2017. One of the successful managements of chronic kidney disease can be seen from the possibility of survival of patients with chronic kidney disease. To identify the probability of survival of an object, survival analysis is used. One method of survival analysis that can be used to determine the survival time of patients with chronic kidney disease is Cox regression. Cox regression must satisfy the proportional hazard assumption, where the ratio of the two hazard values must be constant with time. The graphical method, namely the log-log graph, can be used to test the proportional hazard assumption, but the results are only used as a provisional estimate. In this study, the goodness of fit test was used to test the assumptions by calculating the correlation between the Schoenfeld residuals and the survival time rank. In conclusion, the variables of hypertension and haemodialysis frequency meet the proportional hazard assumption.

Keywords: chronic kidney disease; Cox regression; goodness of fit; log-log graph; proportional hazard assumption

Full Text:

PDF

References

N. Afifah, “Uji Proportional Hazard pada Data Penderita Kanker Serviks di RSUD dr. Soetomo Surabaya”, Jurnal Sains dan Seni ITS. 2016.

N. R. Hill, S. T. Fatoba, J. L. Oke, J. A. Hirst, C. A. O’Callaghan, and D.S. Lasseron, Global Prevalance of Chronic Kidney Diseas – Asystematic Review and Meta-Analysis. PLos iOne. 2016.

Kementrian Kesehatan Republik Indonesia. Info Pusat Data dan Informasi Kementerian Kesehatan RI, Situasi PGK. Jakarta: Kementrian Kesehatan Republik Indonesia. 2017.

S. I. Arifa, M. Azam, dan O. W. K. Handayani, ”Faktor yang Berhubungan dengan Kejadian Penyakit Ginjal Kronik pada Penderita Hipertensi di Indonesia”, Jurnal MKMI, 2017.

B. Mousavie, F. Hayati, and M. J. A. Ansari, ”Survival of Diabetes Patients on Hemodialysis”, Iranian Journal of Kidney Disease, vol. 4 no. 1, pp. 74. 2010.

J. Valdivia, C. Gutierrez, J. Treto, E. Delgado, D. Mendez, I. Fernandez, A. Abdo, L. Perez, M. Forte, and Y. Rodriguez, ”Prognostic Factors in Hemodialysis Patients: Experience of a Havana Hospital”, MEDICC Review, vol. 15 no. 3. 2013.

D. Yulianto dan H. Basuki, ”Analisis Ketahanan pasien PGK dengan Hemodialisis di RSUD Dr. Soetomo Surabaya”, Jurnal Manajemen Kesehatan Yayasan RS. 2017.

D. G. Kleinbaum and M. Klein, Survival Analysis: A Self-Learning Text, Third Edition. New York: Springer. 2012.

D. G. Kleinbaum and M. Klein, Survival Analysis: A Self-Learning Text, Second Edition. New York: Springer. 2005.

N. Ata and M. T. Zoter, “Cox Regression Models with Nonproportional hazards Applied to Lung Cancer Survival Data“, Hacettepe Journal of Mathematics and Statistics. 2007.

P. Marjikoen, P. Tumor Ganas Alat Genital. Jakarta: Yayasan Bina Pustaka Sarwono Prawirohardjo. 2007.

W. J. Connover, Practical Nonparametric Statistic. New York: John Wiley and Sons, 1999.

Refbacks

  • There are currently no refbacks.