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IMPLEMENTASI MODEL ACCELERATED FAILURE TIME (AFT) BERDISTRIBUSI LOG-LOGISTIK PADA PASIEN PENYAKIT JANTUNG BAWAAN

*Dwi Nooriqfina  -  Departemen Statistika, Fakultas Sains dan Matematika, Universitas Diponegoro, Indonesia
Sudarno Sudarno  -  Departemen Statistika, Fakultas Sains dan Matematika, Universitas Diponegoro, Indonesia
Rukun Santoso  -  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.

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Abstract

Log-Logistic Accelerated Failure Time (AFT) model is survival analysis that is used when the survival time follows Log-Logistic distribution. Log-Logistic AFT model can be used to estimate survival time, survival function, and hazard function. Log-Logistic AFT model was formed by regressing covariates linierly against the log of survival time. Regression coefficients are estimated using maximum likelihood method. This study uses data from Atrial Septal Defect (ASD) patients, which is a congenital disease with a hole in the wall that separates the top of two chambers of the heart by using sensor type III. Survival time as the response variable, that is the time from patient was diagnosed with ASD until the first relapse and uses age, gender, treatment status (catheterization/surgery), defect size that is the size of the hole in the heart terrace, pulmonary hypertension status, and pain status as predictor variables. The result showed that variable gender, treatment status, defect size, pulmonary hypertension status, and pain status affect the first recurrence of ASD patients, so it is found that category of female, untreated patient, defect size ≥12mm, having pulmonary hypertension, having chest pain tend to have first recurrence sooner than the other category.

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Keywords: Atrial Septal Defect; Survival Analysis; Log-Logistic Accelerated Failure Time Model.

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  1. Athoillah, I., Wuryandari, T. & Sudarno, 2012. Model Regresi Data Tahan Hidup Tersensor Tipe III Berdistribusi Log-Logistik. Jurnal Gaussian Vol. I, No.1: hal 84-92
  2. Collet, D., 2004. Modelling Survival Data in Medical Research. 2nd ed. London: Chapman and Hall
  3. Danardono, 2012. Analisis Data Survival. Yogyakarta: Universitas Gadjah Mada
  4. Harlan, J., 2017. Analisis Survival. Depok: Gunadarma
  5. Hosmer, D. W., Lemeshow, S. & May, S., 2008. Applied Survival Analysis: Regression Modelling of Time-to-Event Data. 2nd ed. New Jersey: John Wiley and Sons, Inc.
  6. John , P. K. & Melvin , L. M., 2003. Survival Analysis: Techniques for Censored and Turncated Data. 2nd ed. New York: Springer-Verlag New York
  7. Kleinbaum, D. G. & Klein, M., 2005. Survival Analysis: A Self-Leranin Text. 2nd ed. New York: Springer
  8. Kusumawardhani, G. E., Suyono & Santi, V. M., 2018. Analisis Suvival dengan Model Regresi pada Data Tersensor Berdistribusi Log-Logistik. Jurnal Statistika dan Aplikasinya (JSA) Vol. II, No. 2: hal 28-35
  9. Lawless, J. F., 2003. Statistical Models and Methods for Lifetime Data. 2nd ed. Canada: John Wiley and Sons, Inc
  10. Lee, E. T. & Wang, J. W., 2003. Statistical Methods for Survival Data Analysis. 3rd ed. New Jersey: John Wiley & Sons, Inc
  11. Mufidah, A. S. & Purhadi, 2016. Analisis Survival pada Pasien Deman Berdarah Dengue (DBD) di RSU Haji Surabaya. Jurnal Sains dan Seni Vol V, No. 2
  12. Naysilla, A. M., 2017. Komplikasi pada Pasien Atrial Septal Defect Dewasa dengan Survivalitas Alami. Indonesia Jurnal Chest Vol. IV, No. 4: hal 23-34
  13. Wardhana, W. & Boom, C. E., 2017. Penanganan Perioperatif Pasien Penyakit Jantung Kongenital Dewasa dengan ASD, Suspek Hipertensi Pulmonal, LV Smallish. Jurnal Anastesiologi Indonesia Vol. IX, No. 2: hal 71-86

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