Parametric Survival Model on IPB University’s Graduation Data

Muhammad Abdurrasyid Nashiruddin, Hadi Sumarno, Retno Budiarti

Abstract


Graduation is one of the assessment criteria in the college accreditation process. Students who graduate on time will assist in the assessment of college accreditation. This study aims to determine the distribution that best fits student graduation data and determine the best model to analyze the factors that determine student graduation from IPB University. This study presents some parametric models in survival analysis, specifically, the accelerated failure time (AFT) model and the proportional hazard (PH) model. The objective of this research is to compare the performance of PH model and the AFT models in analyzing the significant factors affecting the student graduation at the IPB University. Based on the study's results, the distribution according to student graduation data is the Burr XII distribution, and the best model using the AIC criteria is the PH Burr XII model. The factors that influence the graduation of IPB University students are gender, faculty, GPA, regional origin, and school status.

 


Keywords


Survival analysis; Burr XII Distribution; Proportional hazard model.

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References


Acton, R. (2015). Characteristics of STEM Success: A Survival Analysis Model of Factors Influencing Time to Graduation Among Undergraduate STEM Majors. Business and Economics Honors Papers, 1(1). http://digitalcommons.ursinus.edu/bus_econ_hon/1

Allison, P. (2014). Event History and Survival Analysis (2nd Edition). SAGE Publications, Inc.

Almuhayfith, F. E., Darwish, J. A., Alharbi, R., & Marin, M. (2022). Burr XII Distribution for Disease Data Analysis in the Presence of a Partially Observed Failure Mode. Symmetry 2022, 14(1298), 1–17. https://doi.org/10.3390/sym14071298

Atlam, M., Torkey, H., El-Fishawy, N., & Salem, H. (2021). Coronavirus disease 2019 (COVID-19): survival analysis using deep learning and Cox regression model. Pattern Analysis and Applications, 24(3), 993–1005. https://doi.org/10.1007/s10044-021-00958-0

Awodutire, P. O., Sasanya, B., Ufuoma, O. G., & Balogun, O. S. (2022). Parametric Modelling of Rainfall Return Periods in South Western Nigeria: Survival Analysis Approach. F1000Research 2022, 11(83), 1–14. https://doi.org/10.2139/ssrn.3936312

BAN-PT. (2019). Akreditasi Perguruan Tinggi Kriteria dan Prosedur 3.0.

D’Arrigo, G., Leonardis, D., Abd Elhafeez, S., Fusaro, M., Tripepi, G., & Roumeliotis, S. (2021). Methods to Analyse Time-to-Event Data: The Kaplan-Meier Survival Curve. Oxidative Medicine and Cellular Longevity, 2021(2290120), 1–7. https://doi.org/10.1155/2021/2290120

Guerra, R. R., Peña-Ramírez, F. A., & Cordeiro, G. M. (2021). The weibull burr xii distribution in lifetime and income analysis. Anais Da Academia Brasileira de Ciencias, 93(3), 1–28. https://doi.org/10.1590/0001-3765202120190961

Khan, S. A., & Khosa, S. K. (2015). Generalized log-logistic proportional hazard model with applications in survival analysis. Journal of Statistical Distributions and Applications, 3(1), 1–18. https://doi.org/10.1186/s40488-016-0054-z

Kovacheva, T. P. (2017). Life tables - key parameters and relationships between them. International Mathematical Forum, 12(10), 469–479. https://doi.org/10.12988/imf.2017.7225

Lee, E. T., & Wang, J. W. (2013). Stitistical Methods for Survival Data Analysis (4 edition). John Wiley & Sons, Inc.

Lu, W., Yu, S., Liu, H., Suo, L., Tang, K., Hu, J., Shi, Y., & Hu, K. (2021). Survival analysis and risk factors in COVID-19 patients. Disaster Medicine and Public Health Preparedness, 16(1), 1–15. https://doi.org/10.1017/dmp.2021.82

Masci, C., Giovio, M., & Mussida, P. (2022). Survival Models for Predicting Student Dropout At University Across Time. Education and New Developments 2022, 203–208. https://doi.org/10.36315/2022v1end043

Mendikbud. (2020). Peraturan Menteri Pendidikan dan Kebudayaan Republik Indonesia Nomor 3 Tahun 2020 tentang Standar Nasional Pendidikan Tinggi.

Moore, D. F. (2016). Applied Survival Analysis Using R. Springer International Publishing.

Muse, A. H., Ngesa, O., Mwalili, S., Alshanbari, H. M., & El-Bagoury, A. A. H. (2022). A Flexible Bayesian Parametric Proportional Hazard Model: Simulation and Applications to Right-Censored Healthcare Data. Journal of Healthcare Engineering, 2022(2051642), 1–28. https://doi.org/10.1155/2022/2051642

Pemerintah Indonesia. (2012). Undang-undang Republik Indonesia Nomor 12 Tahun 2012 tentang Pendidikan Tinggi.

Pham, H. (2019). A new criterion for model selection. Mathematics 2019, 7(1215), 1–12. https://doi.org/10.3390/MATH7121215

Provost, S. B., Mansoor, M., Saboor, A., & Cordeiro, G. M. (2018). On The q -Generalized Extreme Value Distribution. REVSTAT-Statistical Journal, 16(1), 45–70.

Saikia, R., & Barman, M. P. (2017). A Review on Accelerated Failure Time Models. International Journal of Statistics and Systems, 12(2), 311–322. http://www.ripublication.com

Staudt, Y., & Wagner, J. (2022). Factors Driving Duration to Cross-Selling in Non-Life Insurance: New Empirical Evidence from Switzerland. Risks, 10(1), 187–207. https://doi.org/10.3390/risks10100187

Syarifuddin, A. (2012). Analisis Survival dan Aplikasinya dalam Bidang Pendidikan (Studi Kasus di Jakarta Selatan). Institut Pertanian Bogor.

Thupeng, W. M. (2016). Use of the Three-parameter Burr XII Distribution for Modelling Ambient Daily Maximum Nitrogen Dioxide Concentrations in the Gaborone Fire Brigade. American Academic Scientific Research Journal, 26(2), 18–32. http://asrjetsjournal.org/index.php/American_Scientific_Journal/article/view/1273

Wang, X., Yue, Y. R., & Faraway, J. J. (2018). Bayesian Regression Modeling with INLA. CRC Press.

Zheng, X., Chiang, J. Y., Tsai, T. R., & Wang, S. (2021). Estimating the failure rate of the log-logistic distribution by smooth adaptive and bias-correction methods. Computers and Industrial Engineering, 156(1), 107188. https://doi.org/10.1016/j.cie.2021.107188

Zoeller, G. E., Drew, B. L., Schmidt, C. W., Peterson, R., & Wilson, J. J. (2021). A paleodemographic assessment of mortality and fertility rates during the second demographic transition in rural central Indiana. American Journal of Human Biology, 34(1), 1–21. https://doi.org/10.1002/ajhb.23571




DOI: https://doi.org/10.31764/jtam.v7i2.12681

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