skip to main content

MODEL REGRESI COX PROPORTIONAL HAZARD PADA DATA KETAHANAN HIDUP PASIEN HEMODIALISA

*Aprilia Sekar Khinanti  -  Departemen Statistika, Fakultas Sains dan Matematika, Universitas Diponegoro, Indonesia
Sudarno Sudarno  -  Departemen Statistika, Fakultas Sains dan Matematika, Universitas Diponegoro, Indonesia
Triastuti Wuryandari  -  Departemen Statistika, Fakultas Sains dan Matematika, Universitas Diponegoro, Indonesia
Open Access Copyright 2021 Jurnal Gaussian under http://creativecommons.org/licenses/by-nc-sa/4.0.

Citation Format:
Abstract

Cox regression is a type of survival analysis that can be implemented with proportional hazard models or duration models. In the survival analysis data, there is a possibility that the data has ties, so it is necessary to use several approaches in estimating the parameters, namely the breslow, efron, and exact approaches. In this study, the Cox proportional hazard regression was used as a method of analysis for knowing the factors that influence the survival time on chronic kidney patients undergoing hemodialysis therapy. Based on the analysis that has been done, the best model is obtained with an exact approach and the factors that influence the survival time of hemodialysis patients are systolic blood pressure, hemoglobin level, and dialysis time. Hemodialysis patients who have high systolic blood pressure have a chance of failing to survive 12,950 times than normal systolic blood pressure.While the hemodialysis patient hemoglobin level increases, the hemodialysis patients chances of failing to survive is 0,6681 times less. Hemodialysis patients who received dialysis therapy with a dialysis time of more than four hours had 0.237 times the chance of failing to survive than patients with a dialysis time of less than or equal to 4 hours.

Keywords: Cox Regression ,Survival, Ties, Hemodialysis.

Fulltext View|Download
Keywords: Cox Regression ,Survival, Ties, Hemodialysis.

Article Metrics:

  1. Collet, D. 2003. Text in Statistical Science: Modelling Survival Data in Medical Research Second Edition. USA : Chapman & Hall
  2. Hafid, H., Bustan, N.M., Aidid, M.K. 2020. Penanganan Ties Event dalam Regresi Cox Proportional Hazard Menggunakan Metode Breslow (Kasus: Pasien Rawat Inap DBD di RSAL Jala Ammari Makassar). VARIANSI: Journal of Statistics and Its Application on Teaching and Research ISSN 2684-7590 (Online) Vol. 5 No. 2 (2020), 13-19 DOI: 10.35580/variansiunm1289
  3. Klein, J.P. dan M.L. Moeschberger. 2003. Survival Analysis Techniques for Censored and Truncated Data Second Edition. USA: Springer
  4. Kleinbaum, D.G. dan Klein, M. 2012. Survival Analysis: A Self-Learning Text Third Edition. New York: Springer
  5. Lee, E.T., dan J.W. Wang. 2003. Statistical Methods for Survival Data Analysis Third Edition. USA: A John Wiley & Sons, Inc
  6. Prabawati, S., Yuki N.N, dan Wahyuningsih, S. 2018. ,Analisis Survival Data Kejadian Bersama dengan Pendekatan Efron Partial Likelihood(Studi Kasus: Lama Masa Studi Mahasiswa Fakultas Matematika dan Ilmu Pengetahuan Alam Universitas Mulawarman Angkatan 2011). jurnal eksponensial Volume9, Nomor1, Mei 2018
  7. Rahmadeni dan Ranti, S., 2016. Perbandingan Model Regresi Cox Menggunakan Estimasi Paramater Efron Partial Likelihood dan Breslow Partial Likelihood. Seminar Nasional Teknologi Informasi, Komunikasi dan Industri (SNTIKI) 8 ISSN : 2085-9902 Pekanbaru,
  8. Setiani, E. 2019. Perbandingan Model Regresi Cox Proportional Hazard Menggunakan Metode Breslow dan Efron (Studi Kasus:Penderita Stroke di RSUD Tugurejo Kota Semarang). Skripsi.Semarang:FMIPA Universitas Diponegoro
  9. Suwitra, K.2014. Buku Ajar Penyakit Dalam Jilid I. Edisi VI. Jakarta: Interna Publishing
  10. Xin, X. 2011. A Study Of Ties And Time Varying Covariates In Cox Proportional Hazard Model. Thesis The University Of Guelph

Last update:

No citation recorded.

Last update:

No citation recorded.