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Estimation of Population Based Colorectal Cancer Survival Analysis Using Cox Proportional Hazards Model
Biomedical Statistics and Informatics
Volume 5, Issue 1, March 2020, Pages: 14-19
Received: Dec. 20, 2019; Accepted: Dec. 30, 2019; Published: Feb. 4, 2020
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Marafa Haliru Muhammad, Planning Division, Usmanu Danfodiyo University Teaching Hospital, Sokoto, Nigeria
Usman Umar, Planning Division, Usmanu Danfodiyo University Teaching Hospital, Sokoto, Nigeria; Department of Mathematics, Usmanu Danfodiyo University, Sokoto, Nigeria
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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.
Colorectal, Cancer, Cox, Hazards, Assumptions
To cite this article
Marafa Haliru Muhammad, Usman Umar, Estimation of Population Based Colorectal Cancer Survival Analysis Using Cox Proportional Hazards Model, Biomedical Statistics and Informatics. Vol. 5, No. 1, 2020, pp. 14-19. doi: 10.11648/j.bsi.20200501.13
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This article is an open access article distributed under the Creative Commons Attribution License ( which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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