Model Survival Semiparametrik dan Parametrik Kasus Data Demam Berdarah
DOI:
https://doi.org/10.23887/jstundiksha.v11i2.43493Keywords:
Analisis survival, DBD, Cox PH, Breslow, Efron, exactAbstract
Data survival merupakan bagian dari time-to-event data. Data survival adalah data longitudinal dimana subjek dipantau dan diikuti dari awal permulaan hingga hingga subjek tersebut mengalami peristiwa yang diinginkan. Demam Berdarah Dengue (DBD) merupakan penyakit infeksi yang disebabkan oleh virus dengue, yang ditularkan dari nyamuk Aedes Spp. Penanganan pasien DBD dengan karakteristik yang dimilikinya perlu dikaji agar untuk mendapatkan informasi dan mengambil langkah yang tepat. Salah satu upaya dari sisi pemodelan adalah dengan menganalisis daya taha (survival) pasien DBD. Penelitian ini bertujuan untuk menganalisis performa model survival parametrik dan semiparametric pada kasus DBD. Metode estimasi Breslow, Efron, dan Exact merupakan pilhan estimasi parameter karena dapat menangani kasus waktu kejadiann kembar (ties). Pemilihan performa model erbaik didasarkan pada Akaike Information Criteria (AIC). Hasil analisis menunjukkan bahwa model terbaik yang diperoleh adalah model semiparametrik Cox PH dengan metode estimasi Exact. Berdasarkan model ini ditemukan bahwa pasien dengan karakteristik berusia lebih muda, kadar hematokrit rendah, kadar hemoglobin tinggi, kadar leukosit rendah , dan suhu badan rendah memiliki laju kesembuhan yang lebih besar dibandingkan dengan pasien dengan karakteristik sebaliknya.
References
Afify, A. Z., & Mohamed, O. A. (2020). A New Three-Parameter Exponential Distribution with Variable Shapes for the Hazard Rate: Estimation and Applications. Mathematics, 8(1), 135. https://doi.org/10.3390/math8010135.
Ahmad, T., Munir, A., Bhatti, S. H., Aftab, M., & Raza, M. A. (2017). Survival analysis of heart failure patients: A case study. PLOS ONE, 12(7), e0181001. https://doi.org/10.1371/journal.pone.0181001.
Aldahlan, M. A. D., & Afify, A. Z. (2020). The Odd Exponentiated Half-Logistic Exponential Distribution: Estimation Methods and Application to Engineering Data. Mathematics, 8(10), 1684. https://doi.org/10.3390/math8101684.
Ali̇, S., Dey, S., Tahi̇R, M. H., & Mansoor, M. (2020). A comparison of different methods of estimation for the Flexible Weibull distribution. Communications Faculty Of Science University of Ankara Series A1Mathematics and Statistics, 794–814. https://doi.org/10.31801/cfsuasmas.597680.
Amir, W. M., Azeem, M., Harun, M. H., Ali, Z., & Shafiq, M. (2020). JMASM 54: A Comparison of Four Different Estimation Approaches for Prognostic Survival Oral Cancer Model. Journal of Modern Applied Statistical Methods, 18(2), 2–14. https://doi.org/10.22237/jmasm/1604189760.
Asar, Ö., Ritchie, J., Kalra, P. A., & Diggle, P. J. (2015). Joint modelling of repeated measurement and time-to-event data: An introductory tutorial. International Journal of Epidemiology, 44(1), 334–344.
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.
Bain, L. J., & Engelhardt, M. (1992). Introduction to probability and mathematical statistics (Vol. 4). Duxbury press Belmont, CA.
Borucka, J. (2014). Methods of handling tied events in the Cox proportional hazard model. Studia Oeconomica Posnaniensia, 2(2), 91–106.
Brembilla, A., Olland, A., Puyraveau, M., Massard, G., Mauny, F., & Falcoz, P.-E. (2018). Use of the Cox regression analysis in thoracic surgical research. Journal of Thoracic Disease, 10(6), 3891–3896. https://doi.org/10.21037/jtd.2018.06.15.
Breslow, N. (1974). Covariance analysis of censored survival data. Biometrics, 89–99.
Bustan, M. N., Aidid, M. K., & Gobel, F. A. (2018). Cox proportional hazard survival analysis to inpatient breast cancer cases. Journal of Physics: Conference Series, 1028(1), 012230.
Collett, D. (2015). Modelling survival data in medical research. CRC press.
Efron, B. (1977). The efficiency of Cox’s likelihood function for censored data. Journal of the American Statistical Association, 72(359), 557–565.
Emmert-Streib, F., & Dehmer, M. (2019). Introduction to survival analysis in practice. Machine Learning and Knowledge Extraction, 1(3), 1013–1038.
Fatekurohman, M., Nurmala, N., & Anggraeni, D. (2018). Comparison of exact, efron and breslow parameter approach method on hazard ratio and stratified cox regression model. 1008(1), 012007.
Fauziah, A. A., Budiman, Safitri, D. L., & Meiza, A. (2021). Survival analysis with the Cox Proportional Hazard Method to determine the factors that affect how long the Large-Scale Social Distancing (LSSD) will applied in various areas affected by the covid-19 pandemic. Journal of Physics: Conference Series, 1751(1), 012004. https://doi.org/10.1088/1742-6596/1751/1/012004.
Flynn, R. (2012). Survival analysis. Journal of Clinical Nursing, 21(19pt20), 2789–2797.
George, B., Seals, S., & Aban, I. (2014). Survival analysis and regression models. Journal of Nuclear Cardiology, 21(4), 686–694.
Gisondi, P., Cazzaniga, S., Di Leo, S., Piaserico, S., Bellinato, F., Pizzolato, M., Gatti, A., Eccher, A., Brunelli, M., Saraggi, D., Girolomoni, G., & Naldi, L. (2021). Impact of the COVID‐19 pandemic on melanoma diagnosis. Journal of the European Academy of Dermatology and Venereology, 35(11).
Guo, S. (2010). Survival analysis. Oxford University Press.
Hilbe, J. M. (2011). Negative binomial regression. Cambridge University Press.
Hughey, J. J., Rhoades, S. D., Fu, D. Y., Bastarache, L., Denny, J. C., & Chen, Q. (2019). Cox regression increases power to detect genotype-phenotype associations in genomic studies using the electronic health record. BMC Genomics, 20(1), 1–7.
Husain, H., Thamrin, S. A., Tahir, S., Mukhlisin, A., & Apriani, M. M. (2018). The application of extended Cox proportional hazard method for estimating survival time of breast cancer. Journal of Physics: Conference Series, 979(1), 012087.
In, J., & Lee, D. K. (2018). Survival analysis: Part I — analysis of time-to-event. Korean Journal of Anesthesiology, 71(3), 182–191. https://doi.org/10.4097/kja.d.18.00067.
Jung, S.-H., Lee, H. Y., & Chow, S.-C. (2018). Statistical Methods for Conditional Survival Analysis. Journal of Biopharmaceutical Statistics, 28(5), 927–938. https://doi.org/10.1080/10543406.2017.1405012.
Kleinbaum, D. G., & Klein, M. (2004). Survival analysis. Springer.
Kleinbaum, D. G., & Klein, M. (2012). Survival analysis: A self-learning text (Vol. 3). Springer.
Lánczky, A., & Győrffy, B. (2021). Web-Based Survival Analysis Tool Tailored for Medical Research (KMplot): Development and Implementation. Journal of Medical Internet Research, 23(7), e27633.
Lee, E. T., & Wang, J. (2003). Statistical methods for survival data analysis (Vol. 476). John Wiley & Sons.
Le-Rademacher, J., & Wang, X. (2021). Time-to-event data: An overview and analysis considerations. Journal of Thoracic Oncology, 16(7), 1067–1074.
Moolgavkar, S. H., Chang, E. T., Watson, H. N., & Lau, E. C. (2018). An assessment of the Cox proportional hazards regression model for epidemiologic studies. Risk Analysis, 38(4), 777–794.
Ozenne, B., Sørensen, A. L., Scheike, T., Torp-Pedersen, C., & Gerds, T. A. (2017). riskRegression: Predicting the risk of an event using Cox regression models. The R Journal, 9(2), 440–460.
Ross, S. M. (2014). Introduction to probability models. Academic press.
Schober, P., & Vetter, T. R. (2018). Survival Analysis and Interpretation of Time-to-Event Data: The Tortoise and the Hare. Anesthesia & Analgesia, 127(3), 792–798. https://doi.org/10.1213/ANE.0000000000003653.
Xie, Y., Han, J., Yu, W., Wu, J., Li, X., & Chen, H. (2020). Survival Analysis of Risk Factors for Mortality in a Cohort of Patients with Tuberculosis. Canadian Respiratory Journal, 2020, 1–9. https://doi.org/10.1155/2020/1654653.
Xin, X. (2011). A study of ties and time-varying covariates in cox proportional hazards model. University of Guelph.
Zhang, Z. (2017). Survival analysis in the presence of competing risks. Annals of Translational Medicine, 5(3), 47–47. https://doi.org/10.21037/atm.2016.08.62.
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