Application of Non Parametric and Semi-Parametric Models on Survival Time of Patients with Cardiovascular Disease: Case Study of Barau Dikko Teaching Hospital, Kaduna, Nigeria
DOI:
https://doi.org/10.56532/mjsat.v5i3.440Keywords:
Kamplan Meier(KM) curve, Log rank Test, Survival function, Hazard function, Cox proportional modelAbstract
This study examines the survival times of cardiovascular patients using Kaplan-Meier (KM) survival curves and the Cox Proportional Hazards model. Analysis of data from Barau Dikko Teaching Hospital, Nigeria, revealed shorter survival times among male patients and those consuming alcohol or smoking. KM curves and Log-rank tests identified significant risk factors such as high blood pressure and irregular pulse rates. Cox model hazard ratios highlighted alcohol consumption as the highest risk factor. These findings demonstrate the utility of non-parametric and semi-parametric models in identifying survival determinants among cardiovascular patients.
References
Mendis, S., Puska, P. & Norrving, B. (2011). Global Atlas on Cardiovascular Disease Prevention and Control (PDF). World Health Organization url: http://apps.who.int/iris/handle/10665/44701
Naghavi, M., Wang, H., Lozano, R., Davis, A., Liang, X. and Zhou, M. (2013). Mortality and Causes of Death Collaborators Global, regional, and national age-sex specific all-cause and cause specific mortality for 240 causes of death, 1990-2013: a systematic analysis for the Global Burden of Disease Study. Lancet. 385(9963);117–171. doi:https://doi.org/10.1016/s0140-6736(14)61682-2)
Wang, H., Naghavi, M., Allen, C., Barber, R.M., Bhutta, Z.A., & Carter, A. (2015). Mortality and Causes of Death Collaborators Global, regional, and national life expectancy, all-cause mortality, and cause-specific
World Health Organization, author. 2023 report. doi: https://doi.org/10.1089/acm.2019.29077.jjw
Adem, A., Demis, T. & Feleke, Y. (2011). Trend of diabetic admissions in Tikur Anbessa and St. Paul’s University Teaching Hospitals from January 2005-December 2009, Addis Ababa, Ethiopia. Ethiopia Med J.; 49 (3);231-8. doi: https://doi.org/10.1210/jendso/bvae208
Spoto, B., D’Arrigo, G., Tripepi, G., Bolignano, D. & Zoccali, C. “Serum gamma-glutamyltransferase, oxidized LDL and mortality in the elderly,” Aging Clinical and Experimental Research, 33(5); 1393–1397. doi: https://doi.org/10.1007/s40520-019-01391-4
Cox, D. R. (1972). Regression Models and Life Tables, Journal of the Royal Statistical Society, Series B, (Methodological), 34(2); 187-220. doi: https://doi.org/10.1111/j.2517-6161.1972.tb00899.x
Musa A., Doguwa.,S & Yahaya A. (2023) comparative study on survival models for obstetric fistula patients in Nigeria. Sule lamido university journal of dcience & technology,7(3). doi: https://doi.org/10.56471/slujst.v7i.478
Edward, H., Achmad, E., Geert, M. & Alain, G. B. (2015). Comparison of risks of cardiovascular events in the elderly using. doi: https://doi.org/10.1186/s12874-015-0004-3
standard survival analysis and multiple-events and recurrent-events methods.BMC Medical Research Methodology. 15(15); 156-167.
Kundu J.P. & Moeschberger, M. L. (2021). Survival Analysis: Techniques for Censored and Truncated Data, 2nd ed. New York: Springer. doi: https://doi.org/10.1007/s42081-021-00115-1
Bart, K., Thomas, R., Thijs, V., Matteo, C. & Daan K. (2022). Accurate training of the Cox proportional hazards model on vertically-partitioned data while preserving privacy. BMC Medical Informatics and Decision Making 22(49). 186-193. doi: https://doi.org/10.1186/s12911-022-01771-3
Alvaro, F. G. & René, B. (2021) Survival analysis of patients with heart failure in the Ecuadorian Andean population. Medwave;21(07);84-94. url: https://repositorio.puce.edu.ec/handle/123456789/4790
Angela, M. C. (2008). Comparison between Weibull and Cox Proportional Hazards Models. A Report submitted in partial fulfillment of the requirements for the degree. Master of Science Department of Statistics College of Arts and Sciences Kansas State University Manhattan, Kansas.
Azme K., Che A., Che H., Mohd A. & Affend A. (2020). Modeling Students’ Performance using Cox and Parametric Accelerated Failure Time Models. Scientific Research Journal (SCIRJ), 8(7); 201-279. doi: http://dx.doi.org/10.31364/SCIRJ/v8.i7.2020.P0720785
Belaynesh, Y.E. & Zeytu, G.A. (2021). Comparison of survival models and assessment of risk factors for survival of cardiovascular patients at Addis Ababa Cardiac Center, Ethiopia: a retrospective study. National Library of Medicine. National Centre of Biotechnology Information. doi: https://doi.org/10.4314/ahs.v21i3.29
Piotr, M. S. & Aaron, J. M. (2022). Scalable algorithms for semiparametric accelerated failure time models in high dimensions. Department of Statistics and Genetics Institute University of Florida, Gainesville, FL. doi: https://doi.org/10.1002/sim.9264
Rana A. W. & Shuaib, M.K. (2006). Comparative Distributions of Hazard Modeling Analysis. Pak. j. stat. oper. res. L(2); 127-134. doi: https://doi.org/10.18187/pjsor.v2i2.96
Samar, A. E., Graziella, D., Daniela, L., Maria, F., Giovanni, T. & Stefanos, R. (2021). Methods to Analyze Time-to-Event Data: The Cox Regression Analysis. Hindawi Oxidative Medicine and Cellular Longevity. 13(11); 6. doi: https://doi.org/10.1155/2021/1302811
Salil, V.D., Vaishali, D. & Varun, S. (2021). Cox proportional hazards model. Indian Journal of Thoracic and Cardiovascular Surgery 37(2); 229–233.
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