Biomedical Statistics and Informatics

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Estimation of Population Based Colorectal Cancer Survival Analysis Using Cox Proportional Hazards Model

Received: 20 December 2019    Accepted: 30 December 2019    Published: 04 February 2020
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Abstract

Colorectal cancer (CRC) is a tumour of the colon and rectum. Most cases of CRC are sporadic; meaning there are no known hereditary (genetic) components, and it develops slowly over several years through adenomatous polyps. Changes in bowel habits, blood in the stool, and anaemia are cardinal symptoms and sings of CRC. In later stages, fatigue, anorexia, weight loss, pain, jaundice, and other signs and symptoms of locally advanced and metastatic disease occur. The aim of this study is to estimate the population based colorectal cancer survival analysis using cox Proportional Hazards model, in order to fits colorectal cancer data in population-based research. This research was a five-year retrospective study on data from a record of colorectal cancer patients that received treatments from 2013 to 2017 in Radiotherapy Department of Usmanu Danfodiyo University Teaching Hospital, Sokoto, being it one of the cancer registries in Nigeria. 9 covariates were selected to fit colorectal cancer data using Cox Regression Models. The 5-year median survival was found to be 121 days. From the results, it was concluded that the predictor variables could significantly predict the survival of colorectal cancer patients using Cox proportional model. Also the results show that the data met Cox Proportional Hazards Assumptions.

DOI 10.11648/j.bsi.20200501.13
Published in Biomedical Statistics and Informatics (Volume 5, Issue 1, March 2020)
Page(s) 14-19
Creative Commons

This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.

Copyright

Copyright © The Author(s), 2024. Published by Science Publishing Group

Keywords

Colorectal, Cancer, Cox, Hazards, Assumptions

References
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[2] Potter, J. D., & Hunter, D. (2008). Colorectal Cancer. In H.-O. Adami, D. Hunter, & D. Trichopoulos, Textbook of Cancer Epidemiology (pp. 275-297). New York: Oxford University Press, Inc.
[3] Arnold, M., Sierra, M. S., Laversanne, M., & Soerjomataram, I. (2017). Global patterns and trends in colorectal cancer incidence and mortality. Gut, pp. 683-691.
[4] Hosmer D. W., Lemeshow S., and May S. (2008). Applied Survival Analysis: Regression Modeling of Time- to- Event Data. 234-654.
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[7] Dickman, P. W. and Hakulinen, T. (2008). Population-Based Cancer Survival Analysis. John Wiley and Sons, UK 23-65.
[8] Kleinbaum D G and Klein M. (2005) Survival Analysis New York: Springer. 16-53.
[9] Dickman, P. W. (2010). An Introduction and Some Recent Development in Statistical Methods for Population-Based Cancer Survival Analysis. Statistical Methods for Population-Based Cancer Survival Analysis. Milan, 33-55.
[10] Adejumo, A. O. and Ahmadu, A. O. (2016) A Study of the Slope of Cox Proportional Hazard and Weibull Models. Science World Journal Vol 11 (No 3) 2016.
[11] Quantin, C., Michal, A., Thierry, M., Gillian, B., Todd, M., Mohammed, A., et al., (1999) Variation over Time of the Effects of Prognostic Factors in a Population based Study of Colon Cancer: Comparison of Statistical Models. American Journal of Epidemiology, Vol. 150, 11-20.
[12] Ahmed E. F., Paul W. V., Don H., (2007) Modeling survival in colon cancer: a methodological review. Molecular Cancer 2007, 6: 15 doi: 10.1186/1476-4598-6-15.
[13] Abdulkabir M., Ahmadu A. O., Udokang A. E., Raji S. T., (2015). Gradient Curve of Cox Proportional Harzard and Weibull Models. Automatic Control of Physiological State and Function, 2: 2 http://dx.doi.org/10.4172/2090-5092.1000108.
[14] Wang Kesheng, Xuefeng Liu, Yue Pan, Daniel Owusu1, Chun Xu. (2017) Comparison of Cox Regression and Parametric Models. Journal of Data Science 16 (2017), 423-442.
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[16] Knut A. M., Dejan Ignjatovic, Marianne A. M. (2017) Tailored Treatment of Colorectal Cancer: Surgical, Molecular, and Genetic Considerations. Clinical Medicine Insights: Oncology. DOI: 10.1177/1179554917690766.
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[18] Zaki A. (2015) Log-linearity for Cox’sregression model. University of Oslo.
[19] Sylla BS, Wild CP (2011). A million Africans a Year Dying from Cancer by 2030: What can cancer research and control offer to the continent? Int J Cancer.
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Author Information
  • Planning Division, Usmanu Danfodiyo University Teaching Hospital, Sokoto, Nigeria

  • Planning Division, Usmanu Danfodiyo University Teaching Hospital, Sokoto, Nigeria; Department of Mathematics, Usmanu Danfodiyo University, Sokoto, Nigeria

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  • APA Style

    Marafa Haliru Muhammad, Usman Umar. (2020). Estimation of Population Based Colorectal Cancer Survival Analysis Using Cox Proportional Hazards Model. Biomedical Statistics and Informatics, 5(1), 14-19. https://doi.org/10.11648/j.bsi.20200501.13

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    ACS Style

    Marafa Haliru Muhammad; Usman Umar. Estimation of Population Based Colorectal Cancer Survival Analysis Using Cox Proportional Hazards Model. Biomed. Stat. Inform. 2020, 5(1), 14-19. doi: 10.11648/j.bsi.20200501.13

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    AMA Style

    Marafa Haliru Muhammad, Usman Umar. Estimation of Population Based Colorectal Cancer Survival Analysis Using Cox Proportional Hazards Model. Biomed Stat Inform. 2020;5(1):14-19. doi: 10.11648/j.bsi.20200501.13

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  • @article{10.11648/j.bsi.20200501.13,
      author = {Marafa Haliru Muhammad and Usman Umar},
      title = {Estimation of Population Based Colorectal Cancer Survival Analysis Using Cox Proportional Hazards Model},
      journal = {Biomedical Statistics and Informatics},
      volume = {5},
      number = {1},
      pages = {14-19},
      doi = {10.11648/j.bsi.20200501.13},
      url = {https://doi.org/10.11648/j.bsi.20200501.13},
      eprint = {https://download.sciencepg.com/pdf/10.11648.j.bsi.20200501.13},
      abstract = {Colorectal cancer (CRC) is a tumour of the colon and rectum. Most cases of CRC are sporadic; meaning there are no known hereditary (genetic) components, and it develops slowly over several years through adenomatous polyps. Changes in bowel habits, blood in the stool, and anaemia are cardinal symptoms and sings of CRC. In later stages, fatigue, anorexia, weight loss, pain, jaundice, and other signs and symptoms of locally advanced and metastatic disease occur. The aim of this study is to estimate the population based colorectal cancer survival analysis using cox Proportional Hazards model, in order to fits colorectal cancer data in population-based research. This research was a five-year retrospective study on data from a record of colorectal cancer patients that received treatments from 2013 to 2017 in Radiotherapy Department of Usmanu Danfodiyo University Teaching Hospital, Sokoto, being it one of the cancer registries in Nigeria. 9 covariates were selected to fit colorectal cancer data using Cox Regression Models. The 5-year median survival was found to be 121 days. From the results, it was concluded that the predictor variables could significantly predict the survival of colorectal cancer patients using Cox proportional model. Also the results show that the data met Cox Proportional Hazards Assumptions.},
     year = {2020}
    }
    

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    AU  - Marafa Haliru Muhammad
    AU  - Usman Umar
    Y1  - 2020/02/04
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    T2  - Biomedical Statistics and Informatics
    JF  - Biomedical Statistics and Informatics
    JO  - Biomedical Statistics and Informatics
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    AB  - Colorectal cancer (CRC) is a tumour of the colon and rectum. Most cases of CRC are sporadic; meaning there are no known hereditary (genetic) components, and it develops slowly over several years through adenomatous polyps. Changes in bowel habits, blood in the stool, and anaemia are cardinal symptoms and sings of CRC. In later stages, fatigue, anorexia, weight loss, pain, jaundice, and other signs and symptoms of locally advanced and metastatic disease occur. The aim of this study is to estimate the population based colorectal cancer survival analysis using cox Proportional Hazards model, in order to fits colorectal cancer data in population-based research. This research was a five-year retrospective study on data from a record of colorectal cancer patients that received treatments from 2013 to 2017 in Radiotherapy Department of Usmanu Danfodiyo University Teaching Hospital, Sokoto, being it one of the cancer registries in Nigeria. 9 covariates were selected to fit colorectal cancer data using Cox Regression Models. The 5-year median survival was found to be 121 days. From the results, it was concluded that the predictor variables could significantly predict the survival of colorectal cancer patients using Cox proportional model. Also the results show that the data met Cox Proportional Hazards Assumptions.
    VL  - 5
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    ER  - 

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