DOI: https://doi.org/10.14710/j.gauss.v11i2.35465

ANALISIS LAJU PERBAIKAN KONDISI KLINIS PASIEN STROKE MENGGUNAKAN REGRESI HAZARD ADITIF LIN-YING (Studi Kasus: Data Pasien Stroke di RSUD Pandan Arang Boyolali Periode Januari 2021 - Agustus 2021)

*Alfiya Nurwidi Hastuti  -  Departemen Statistika, Fakultas Sains dan Matematika, Universitas Diponegoro, Indonesia
Yuciana Wilandari  -  Departemen Statistika, Fakultas Sains dan Matematika, Universitas Diponegoro, Indonesia
Sudarno Sudarno  -  Departemen Statistika, Fakultas Sains dan Matematika, Universitas Diponegoro, Indonesia
Open Access Copyright 2022 Jurnal Gaussian under http://creativecommons.org/licenses/by-nc-sa/4.0.

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Abstract
Additive hazard regression is a survival analysis that is an alternative to Cox proportional hazard regression. The additive hazard models that have been developed include the Aalen additive hazard model and the Lin-Ying. In this study, Lin-Ying additive hazard regression was used as an analytical method to be applied in stroke data that had been hospitalized at Pandan Arang Hospital Boyolali. This method is considered more effective because there is no assumption of proportionality. The purpose of using this method in this study are analyze the characteristics of stroke patients, form a Lin-Ying additive hazard regression model, find out the factors that affect the rate of improvement of the clinical condition of stroke patients, and interpret the model. Based on the analysis that has been done, the average length of hospitalization is 4,471 days ≈ 4 days, and the factors that significantly affect the rate of improvement of clinical conditions in stroke patients at Pandan Arang Hospital Boyolali are blood pressure and blood sugar.
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Keywords: Stroke; Survival; Additive Hazard Regression; Lin-Ying

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  1. Collet, D. 2003. Modelling Survival Data in Medical Research Second Edition. US: Chapman & Hall
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  3. Johnson, W et al. 2016. Stroke: a global response is needed https://www.researchgate.net/publication/308906190_Stroke_A_global_response_is_needed. Diakses pada 7 Oktober 2021 pukul 21.00
  4. Kemenkes RI. 2019. Hasil Utama RISKESDAS 2018. Jakarta: Kementerian Kesehatan Republik Indonesia
  5. Klein, J.P., dan Moeschberger, M.L. 2003. Survival Analysis Techniques for Censored and Truncated Data Second Edition. Springer-Verlag, New York, Inc
  6. Klein, M., dan Kleinbaum, D.G. 2012. Survival Analysis: A Self-Learning Text. Third Edition. New York: Springer Science and Business Media, Inc
  7. Lee E.T., dan Wang, J.W. 2003. Statistical Methods For Survival Data Analysis. Third Edition. New Jersey: John Wiley and Sons
  8. Lin, D.Y., dan Ying, Z. 1994. Semiparametric Analysis of the Additive Risk Model. Jurnal Biometrika Vol. 1, No. 81: 61-71
  9. Madadizadeh, F., Ghanbarnejad, A., Ghavami, V., Bandamiri, M. Z., & Mohammadianpanah, M. (2017). Applying additive hazards models for analyzing survival in patients with colorectal cancer in Fars Province, Southern Iran. Asian Pacific Journal of Cancer Prevention, 18(4), 1077–1083
  10. Maruddani, D.A.I., Tarno., Hoyyi, A., Rahmawati, R., dan Wilandari, Y. 2021. Survival Analysis. Semarang: UNDIP Press Semarang
  11. Nastiti, D. 2012. Gambaran Faktor Risiko Kejadian Stroke pada Pasien Stroke Rawat Inap di Rumah Sakit Krakatau Medika Tahun 2011. Skripsi. Depok: Universitas Indonesia
  12. Qomaria, T., Fatekurohman, M., dan Anggraeni, D. 2019. Aplikasi Model Cox Proportional Hazard pada Pasien Stroke RSD Balung Kabupaten Jember. Indonesian Journal of Applied Statistics Vol. 2, No. 2: 94-112
  13. Sacco, R. L et al. 2013. An Updated Definition of Stroke for the 21st Century. AHA Journals Vol. 44, No. 27: 2064-2089
  14. Sofyan, A.M., Sihombing, I.Y., dan Hamra, Y. 2013. Hubungan Umur, Jenis Kelamin, dan Hipertensi dengan Kejadian Stroke. Jurnal Ilmiah Fakultas Kedokteran Universitas Halu Oleo Vol. 1, No. 1: 24-30
  15. Sulistyani, D.O., dan Purhadi. 2013. Analisis Terhadap Faktor-Faktor yang Mempengaruhi Laju Perbaikan Kondisi Klinis Pasien Penderita Stroke dengan Regresi Cox Weibull. JURNAL SAINS DAN SENI POMITS Vol. 2, No. 1: 2337-3520
  16. Utami, P. 2009. Solusi Sehat Mengatasi Stroke. Jakarta: Agromedia Pustaka
  17. WHO. 2019. International Statistical Classification of Diseases and Related Health Problems 10th Revision (ICD-10)-WHO Version for: 2019-covid expanded. https://icd.who.int/browse10/2019/en#/I60.0. Diakses pada 9 Oktober 2021 pukul 11.00
  18. Wuryandari, T., Kartiko, S.H., dan Danardono. 2020. Analisis Survival untuk Durasi Proses Kelahiran menggunakan Model Regresi Hazard Additif. Jurnal Gaussian Vol. 9, No. 4: 402-410

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