Analisis Data Tersensor Kanan dengan Metode Cox Proportional Hazard Model

La Ode Nashar, Asriadi Asriadi, Dewi Rahmawaty Isa

Abstract


In time series data research, it is often encountered the problem of lack of available time and budget. So often found the presence of censored data. Censored data can be analyzed using the partial likelihood method. However, other obstacles will arise if the processed data contains ties. So it is necessary to use other methods that can overcome the co-occurrence. In this study, the Breslow method is used to estimate the censored data due to joint events. This method is applied to the case of patients with burns. The variables used are the method of treatment, gender, race and type of burn. The results of the analysis showed that race did not give a significant difference to the incidence of cutting burned organs. While the other three variables showed significant differences at the 95% confidence level. The results of the analysis using the Cox proportional hazard method showed that patients treated with the body cleansing method had 17% greater risk of cutting than those treated with the body routhine bathing.

 


Full Text:

PDF


DOI: https://doi.org/10.15548/jostech.v2i2.4434
Abstract views : 177 times
PDF : 45 times

References


P. Schober and T. R. Vetter, “Survival analysis and interpretation of time-to-event data: The tortoise and the hare,” Anesth. Analg., vol. 127, no. 3, pp. 792–798, 2018, doi: 10.1213/ANE.0000000000003653.

M. L. Klein, John P and Moeschberger, Survival Analysis Techniques for Censored and Truncated Data. New York: Springer, 2019.

L. Myers, “Encryption 101: What is it? When should I use it?,” We Live Secur. , vol. 42, no. 1, p. 3713, 2016, [Online]. Available: http://www.welivesecurity.com/2016/02/04/encryption-101-useful/

S. Rai, P. Mishra, and U. C. Ghoshal, “Survival analysis: A primer for the clinician scientists,” Indian J. Gastroenterol., vol. 40, no. 5, pp. 541–549, 2021, doi: 10.1007/s12664-021-01232-1.

A. R. Faisal, M. N. Bustan, and S. Annas, “ANALISIS SURVIVAL DENGAN PEMODELAN REGRESI COX PROPORTIONAL HAZARD MENGGUNAKAN PENDEKATAN BAYESIAN (Studi Kasus: Pasien Rawat Inap Penderita Demam Tifoid di RSUD Haji Makassar),” VARIANSI J. Stat. Its Appl. Teach. Res., vol. 2, no. 2, p. 62, 2020, doi: 10.35580/variansiunm14629.

T. Wuryandari, “Model regresi cox proporsional hazard pada data durasi proses kelahiran dengan ties,” vol. 9, no. 1, 2021.

I. Kuitunen, V. T. Ponkilainen, M. M. Uimonen, A. Eskelinen, and A. Reito, “Testing the proportional hazards assumption in cox regression and dealing with possible non-proportionality in total joint arthroplasty research: methodological perspectives and review,” BMC Musculoskelet. Disord., vol. 22, no. 1, pp. 1–7, 2021, doi: 10.1186/s12891-021-04379-2.

S. W. Purnami and I. N. Pertiwi, “Regresi Cox Proportional Hazard Untuk Analisis Survival Pasien Kanker Otak di C-Tech Labs Edwar Technology Tangerang,” Inferensi, vol. 3, no. 2, p. 65, 2020, doi: 10.12962/j27213862.v3i2.7727.

S. A. Purba, “Estimasi Parameter Data Berdistribusi Normal Menggunakan Maksimum Likelihood Berdasarkan Estimation of Normal Distributed Data Parameters Using the Maximum Likelihood Based on Newton Raphson,” vol. 9, no. 1, pp. 16–18, 2020.

L. O. S. Tedy Machmud, La Ode Nashar, Dina Fakhriyana, “ESTIMASI PARAMETER COX SEMIPARAMETRIC HAZARDS MODEL DENGAN METODE EFRON PADA,” J. Mat. UNAND, vol. 10, no. 3, pp. 394–405, 2021.

G. E. Kusumawardhani, V. M. Santi, and S. Suyono, “Analisis Survival dengan Model Regresi pada Data Tersensor Berdistribusi Log-Logistik,” J. Stat. dan Apl., vol. 2, no. 2, pp. 28–35, 2018, doi: 10.21009/jsa.02204.

D. Tresnawanti et al., “Fungsi Likehood Pada Data Tersensor Interval Univariat ( Likelihood Function For Univariat Interval Censored Data ),” pp. 0–3.

H. Hafid, M. N. Bustan, and M. K. Aidid, “Penanganan Ties Event dalam Regresi Cox Proportional Hazard Menggunakan Metode Breslow (Kasus: Pasien Rawat Inap DBD di RSAL Jala Ammari Makassar),” VARIANSI J. Stat. Its Appl. Teach. Res., vol. 2, no. 1, p. 13, 2020, doi: 10.35580/variansiunm12897.

A. N. Vitriana and R. Kusumawati, “MODEL COX EXTENDED UNTUK MENGATASI NONPROPORTIONAL HAZARD PADA KEJADIAN BERSAMA,” vol. 7, no. 1.

E. Setiani, S. Sudarno, and R. Santoso, “PERBANDINGAN MODEL REGRESI COX PROPORTIONAL HAZARD MENGGUNAKAN METODE BRESLOW DAN EFRON (Studi Kasus: Penderita Stroke di RSUD Tugurejo Kota Semarang),” J. Gaussian, vol. 8, no. 1, pp. 93–105, 2019, doi: 10.14710/j.gauss.v8i1.26624.


Refbacks

  • There are currently no refbacks.


Copyright (c) 2022 JOSTECH: Journal of Science and Technology

Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.