Published July 30, 2025
| Version CC-BY-NC-ND 4.0
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Open
Survival Model of Cervical Cancer Patients using the 3-Parameter Weibull Distribution Model
Creators
- 1. Assistant Lecturer, Department of Local Government Accounting and Finance, Local Government Training Institute (LGTI), Dodoma, Tanzania.
Contributors
Contact person:
Researchers:
- 1. Assistant Lecturer, Department of Local Government Accounting and Finance, Local Government Training Institute (LGTI), Dodoma, Tanzania.
- 2. Lecturer, Department of Local Government Accounting and Finance, Local Government Training Institute (LGTI), Dodoma, Tanzania.
- 3. Department of Local Government Accounting and Finance, Local Government Training Institute (LGTI), Dodoma, Tanzania.
Description
Abstract: This study aimed to evaluate a parametric survival model for cervical cancer patients treated with ORCI, and a case study was conducted to describe the model. The survey of survival times of cervical cancer patients may help reduce cervical cancer outcomes. Data on socio-demographic characteristics, reproductive status, stages, treatment, and follow-up of the treatment, abstracted from medical files, were considered in model development. The primary objective of this study was to analyse cervical cancer survival times from the diagnosis period using a three-parameter Weibull distribution model. The analysis was performed using the open-source statistical software R and Minitab. The three-parameter Weibull distribution is highly flexible for fitting random data; moreover, it exhibits strong adaptability to various types of probability distributions. When the three parameters are well chosen, it can be equal to or approximate some other statistical distribution. However, the three parameters were estimated to utilize the Weibull model successfully. The distribution of survival times of cervical cancer patients, as analysed, follows the three-parameter Weibull distribution, with required test statistics including the Anderson-Darling significant value and standard probability plots. The use of other parametric distribution models, such as the Gamma, three-parameter Gamma, and Weibull distributions, which encompass various types of hazard functions, is recommended for future studies.
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Additional details
Identifiers
- DOI
- 10.54105/ijpmh.D1068.05050725
- EISSN
- 2582-7588
Dates
- Accepted
-
2025-07-15Manuscript received on 07 March 2025 | First Revised Manuscript received on 18 April 2025 | Second Revised Manuscript received on 19 June 2025 | Manuscript Accepted on 15 July 2025 | Manuscript published on 30 July 2025.
References
- Andersen P.K., organ O., Gill R.D., Keiding N. (1993); Statistical Models Based On Counting Processes, Springer, New York. DOI: http://dx.doi.org/10.1007/978-1-4612-4348-9
- Collet D. (2003); Modelling survival data in medical research, Chapman and Hall, London. DOI: https://doi.org/10.1201/b18041
- Cox D.R. (1975); Partial likelihood, Biometrika, Vol. 62, No. 2, 269- 276, DOI: https://doi.org/10.1093/biomet/62.2.269
- Cox, D. R (1972): Regression models and life tables. Journal of the Royal Statistical Society: Series B, 34:187-220, DOI: https://doi.org/10.1111/j.2517-6161.1972.tb00899.x
- Everitt B.S. and Hothorn T. (2005); A handbook of Statistical analyses using R, CRC Press, London. https://digitallibrary.tsu.ge/book/2019/september/books/A-Handbookof-Statistical-Analyses.pdf
- Harrel F.E., (2001); Regression modeling strategies: With applications to linear models, logistic regression and survival analysis, Springer, New York. DOI: https://doi.org/10.1007/978-1-4757-3462-1
- Hosmer D.W and Lemeshow S (1999); Applied survival analysis: Regression modeling to time to event data, John Wiley & Sons Inc, New York. DOI: https://doi.org/10.1002/9780470258019
- Izenman A.J. and Tran L.T. (1990); Estimation of the survival function and hazard rate, Journal of Statistical Planning and Inference, Vol 24, 233-247. DOI: https://doi.org/10.1016/0378-3758(90)90044-U
- Kalbfleish & Prentice (2002); The statistical analysis of failure time data, 2nd edition, Wiley and Sons, New York. DOI: http://doi.org/10.1002/9781118032985
- Kardaum, (1993); Statistical analysis of male larynx cancer patients: A case study. Statistical Nederlandica, 37:103-126. https://www.iosrjournals.org/iosrjhss/papers/Vol.%2021%20Issue5/Version-1/E0215012234.pdf
- Lee E. T. and Wang J. W. (2003); Statistical Methods for Survival Data Analysis, 3 rd edition, Wiley & Sons Inc, New Jersey. DOI: http://doi.org/10.1002/0471458546
- Leung, K.M., R.M. Elashoff and A.A. Afifi, (1997); Censoring issues in survival analysis, Annual Review of Public Health, 18:83-104, DOI: https://doi.org/10.1146/ANNUREV.PUBLHEALTH.18.1.83
- Lwanga S.K. and Lemeshow S. (1991); Sample size determination in health studies: A practical guide, WHO manual. https://iris.who.int/handle/10665/40062
- Schafer J. L. (2002); Analysis of Incomplete Multivariate Data, Chapman and Hall, London. https://www.taylorfrancis.com/books/mono/10.1201/9780367803025/a nalysis-incomplete-multivariate-data-schafer
- Schoenfeld D. (1982), Residuals for the proportional hazards regression model, Biometrika, 69(1): 239-241, DOI: http://dx.doi.org/10.1093/biomet/69.1.239
- Sharma S. (1996); Applied multivariate techniques, John Wiley & Sons Inc., Canada. https://www.wiley.com/en-us/Applied+Multivariate+Techniques+-p9780471310648
- Swaminathan R, et al (2002); Effect of loss to follow-up on populationbased cancer survival rates in developing countries, International Journal of Cancer Suppl, 13:172. DOI: https://doi.org/10.2471/blt.07.046979
- Swaminathan R, Lucas E and Sankaranarayanan R (2011); Cancer survival in Africa, Asia, the Caribbean and Central America: Database and Attributes, IARC Scientific Publications No. 162, International Agency for Research on Cancer, Lyon. https://pubmed.ncbi.nlm.nih.gov/21675403/
- Theaune, T. and Gambach (2001); Modelling survival data- Extending the Cox model, Springer, London. https://catalog.nlm.nih.gov/discovery/fulldisplay?docid=alma99109603 53406676&context=L&vid=01NLM_INST:01NLM_INST&lang=en& search_scope=MyInstitution&adaptor=Local%20Search%20Engine&t ab=LibraryCatalog&query=lds56,contains,Models,%20Statistical,AND &mode=advanced&offset=110
- Walter A. S. and Samuel S. W. (2004); Weibull Models, John Wiley and Sons, New York. https://www.wiley.com/en-us/Weibull+Models+-p-9780471360926
- WHO (2007). The World Health Organisation's fight against cancer: Strategies that prevent, cure, and care. WHO Cancer Brochure. https://www.afro.who.int/sites/default/files/2017- 06/WHOCancerBrochure2007.FINAL_.pdf
- WHO, Human Papillomavirus (HPV) and Cervical Cancer, s.l.: World Health Organization (WHO). 2013. https://www.who.int/news-room/fact-sheets/detail/human-papillomavirus-and-cancer
- WHO/ICO, HPV Information Centre on HPV and Cervical Cancer (HPV Information Centre), 2010, WHO. https://hpvcentre.net/
- Woolson, R.F., (1981); Rank test and a one-sample log-rank test for comparing observed survival data to standard population. Biostatistics, 37:687-696. DOI: http://doi.org/10.2307/2530150
- World Health Organisation (WHO), 2008, The Global Burden of Disease: 2004 Update. Geneva: World Health Organization. https://www.who.int/publications/i/item/9789241563710
- Zhou Mai (2000); Using software R for survival analysis and simulation —a tutorial, The American Statistician, Kentucky. https://www.ms.uky.edu/~mai/rsurv.pdf
- Weibull, W (1951). A statistical Distribution of wide applicability, Journal of Applied Mechanics, 18, 293-297. DOI: https://doi.org/10.1115/1.4010337
- GLOBOCAN (2012); Cancer Incidence and Mortality Worldwide, GLOBOCAN, Lyon, France: International Agency for Research on Cancer. https://publications.iarc.fr/Databases/Iarc-Cancerbases/GLOBOCAN2012-Estimated-Cancer-Incidence-Mortality-And-PrevalenceWorldwide-In-2012-V1.0-2012
- Plummer M, de Martel C, Vignat J, Ferlay J, Bray F, Franceschi S. (2016): Global burden of cancers attributable to infections in 2012: a synthetic analysis. Lancet Global Health, London. DOI: https://doi.org/10.1016/s2214-109x(16)30143-7
- Yang F, Ren H, and Hu Z (2019) Maximum Likelihood Estimation for Three-Parameter Weibull Distribution using Evolutionary Strategy, Mathematical Problems in Engineering. DOI: http://dx.doi.org/10.1155/2019/6281781