Original Article

Comparison of Parametric Models: Appication to Hypertensive Patients in a Teaching Hospital, Awka

Abstract

Introduction: In Nigeria, hypertension is a common sickness among grownups. This research was carried out to determine the best model for predicting survival of hypertensive patients using goodness of fit criteria, Standard Error (SE), Akaike Information Criterion (AIC) and Bayesian Information Criterion (BIC).
Method: A total of 105 patients who were diagnosed with hypertension from January 2013 to July 2018 were considered in which death is the event of interest. Six parametric models such as; exponential, Weibull, Lognormal, Log-logistic, Gompertz and hypertabastic distribution were fitted to the data using goodness of fit such as S.E, AIC and BIC to determine the best model. The parametric models were considered because they are all lifetime distributions.
Results:The result shows that the hypertabastic distribution has the lowest AIC and BIC, followed by Gompertz distribution. The standard error also indicates the hypertabastic model is better because it has the least value of standard error. This indicates that in terms of relative efficiency and parameterization the hypertabastic model is the best. The Survival Probability Plot of the six parametric models shows that the Hypertabastic distribution best fitted the data because it shows a clear step function than the other distribution and this justifies the result SE, AIC and BIC presented.
Conclusion: Since hypertabastic distribution has the lowest SE, AIC and BIC it indicates that it is the best parametric model for predicting survival of hypertensive patients in chukwuemeka Odumegwu Ojukwu university teaching hospital Awka, Nigeria.

1.KleinbaumD.G and Klein M. Survival Analysis: A Self Learning Text, Third Edition. New York: Springer-Verlag, 2012.
2. Collett D.Modeling Survival Data in Medical Research, first Edition.London, UK. Chapman Hall, 1994
3. Tabatabai M.A, Zoran B, David K.W and Karam S.P.Hypertabastic Survival Model.Theoretical Biology and Medical Modeling, 2007, 4: 40, pp 1-13.
4. Gardiner J. Survival Analysis: Overview of Parametric, Nonparametric and Semi
parametric approaches and New Developments.SAS Global Forum 2010.Statistics and Data Analysis. Pp 1-25..
5. Sadegh K.M, Shahnaz R, Jamileh A and Iraj H Comparing of Cox Model and Parametric Models In Analysis of Effective factors on Event Time of Neuropathy in Patients with type 2 Diabetes. Journal of Research in Medical sciences, 2007, 22:115, pp 1-8.
6. Vallinayagam V, Prathap S, Venkatesa P. Parametric Regression Models in the Analysis of Breast Cancer Survival Data. International Journal of Science and Technology, 2014, Vol. 3, No. 3, ISSN 2049-7318, pp 163-167.
7. Shankar P.K, Sreenivas V,Subrat K.A. Accelerated Failure Time Models: An Application in the Survival of Acute Liver Failure of Patients in India.International Journal of Science and Research, ISSN 2319-7064, Vol.3, Issue 6, 2014, pp 161-166.
8. Ali Z, Mostafa H, Mahmood M, Kazem M, Hojjat Z and Kouroush H.N. A comparison Between Accelerated Failure Time and Cox Proportional Hazard Models in Analyzing the Survival of Gastric Cancer Patients.Iran Journal of Public Health, 2015, Vol. 44, No. 8. PP 10951102.
9. Maryam M, Jamshid Y.C and Fateme E. Application of Parametric Models to a Survival Analysis of Hemadialysis Patients.Nephrourol, 2016, 8(6): e28738, pp 1-6.
10. Asrin K, Ali D, Kourosh S. Application of Accelerated Failure Time Models for Breast Cancer of Patients’ Survival in Kurdistan Province of Iran.Journal of Cancer Research and Therapeutics.2016, Vol. 12, Issue 3, pp 11841188.
11. Philip O.A, Oladapo A.K, Oluwatosin R. Parametric Modeling of Survival Analysis among Breast Cancer Patients in a Teaching Hospital, Osogbo.Journal of Cancer Treatment and Research, 2017, Vol. 5, No.5, pp. 81-85.
12. Abolfrazl N, Jamshid Y.C, Iradj M, Hosien R and Alireza K. Parametric Model Evaluation in Examining the Survival of Gastric Cancer Patients Andits Influencing Factors.Global Journal Health Science, 2017, Vol.9, No. 3, ISSN 1916-1736, pp 260-265.
13. Jaber J. Credit Risk Assessment Using Survival Analysis for Progressive Right – Censored Data: A Case Study in Jordan.Journal of Internet Banking and Commerce, 2017, Vol. 22, No. 1, pp 1-18
14. Soraya M, Ahmad R.B, Mohamad A.P and Ali A.K. Application of the Parametric
Regression Model with the Four Parameter Loglogistic Distribution for Determining of the Effecting Factors on the Survival Rate of Colorectal Cancer Patients in the Presence of Competing Risks. Iran Red Crescent Medical Journal, 2017, Vol. 19 (6): e55609, pp 1-8.
15. Syahila E.A, Abdullah M, Kek S.L and Muhammad J. Analysis of Survival in Breast Cancer Patients by Using Different Parametric Models.journal of Physics: Conference Series 2017, 890, 012169, pp 1-7.
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IssueVol 5 No 2 (2019) QRcode
SectionOriginal Article(s)
DOI https://doi.org/10.18502/jbe.v5i2.2341
Keywords
Survival analysis Censorin Parametric models Hypertabasti
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How to Cite
1.
Ibenegbu A, Osuji G, Umeh E. Comparison of Parametric Models: Appication to Hypertensive Patients in a Teaching Hospital, Awka. JBE. 2020;5(2):110-119.