RISK FACTOR ANALYSIS OF BREAST CANCER PATIENTS IN A NIGERIAN TERTIARY HOSPITAL
Abstract
This research attempts to determine the survival rate and identify the risk factors affecting the survival of breast cancer patients. The study used 334 breast cancer patients from University College Hospital (UCH), Ibadan. The Kaplan-Meier estimator result shows that 70 patients survived after treatment, the median survival age is 58 years old and the median survival time is 1423 days which is equivalent to 4 years with survival probability of 0.455. Three survival models namely; Cox proportional, Weibull and Log-logistic regression models were fitted to the data. Cox proportional model has the lowest value of Akaike Information Criterion (AIC) (724.6865) and Bayesian Information Criterion (BIC) (745.1765) is considered the best fitted model. The result of Cox proportional model shows that the prognostic factors affecting the risk of dying from breast cancer are Histology (MC), Tumor stage II, Surgical type (MRM) and Surgical type (SM).
References
Adeboye, N. O., Ajibode, I. A. and Aako, O. L. (2020). On the Survival Assessment of Asthmatic Patients Using Parametric and Semi-Parametric Survival Models. Occupational Diseases and Environmental Medicine, 8, 50-63. DOI: 10.4236/odem.2020.82004.
Anders, C. K., Johnson, R., Litton, J., Phillips, M. and Bleyer, A. (2009). Breast cancer
before age 40 years. Semin Oncol.; 36(3): 237-249. doi: 10.1053/j.semioncol.2009.03.001.
El Saghir, S., Adebamowo, A., Anderson, O., Carlson, W., Bird. A., Corbex, M., Badwe, A., Bushnaq, A., Eniu, A., Gralow, R., Harness, K., Masetti, R., Perry, F., Samiei, M., Thomas, B., Wiafe-Addai, B. and Cazap, E. (2011). Breast cancer management in low resource countries (LRCs): consensus statement from the Breast Health Global Initiative. Supplement, 20(2), S3-S11.
Ezekiel, I. D. and Aako, O. L. (2018). Comparison of Cox’s and Weibull Regression Models in Assessing the Prognostic Factors for Survival of Asthmatic Patients. International Journal of Current Innovation Research. 4, 11(A), 1390-1394. DOI: 10.24327/IJCIR.
Falgore, J.Y., Kajuru, J. Y., Usman, M. and Ramadan, K. M. (2022). Accessing of Some Risk Factors of Rheumatoid Arthritis Using Logistic Regression. FUDMA Journal of Sciences (FJS), 6(1), 2022,102-106. DOI: https://doi.org/10.33003/fjs.
Kaplan, E. L. and Meier, P. (1958). Non-parametric estimation from incomplete observations. J. American Statistical Association, 53, 457-481.
Kleinbaum, D. G. and Klien, M. (2012). Survival Analysis: A Self – Learning Text. 3rd edition, Statistics for Biology and Health, USA. Springer.
McPherson, K., Steel, M., and Dixon, M. (2000). ABC of Breast Diseases: breast cancer-epidemiology, risk factors, and genetics. British Medical Journal, 21(4), 624.
Moghimi-Dehkordi, B., Safaee, A., Pourhoseingholi, M. A., Fatemi, R., Tabeie, Z. and Zali, M. R. (2008). Statistical Comparison of Survival Models for Analysis of Cancer Data. Asian Pacific Journal of Cancer Prevention, 9, 417-420.
Montsarrat, R., Sandra, L., Ester, V., Montserrat, M., Misericordia, C., Rafael, M., Roger, P. and Josep-Alfons, E. (2009). Effectiveness of Early Detection of Breast Cancer Mortality Reduction in Catalonia (Spain). BMC Cancer, 9(3), doi:10.1186/1471-2407-9-326.
Musa, M. C., Asiribo, O. E., Dikko, H. G., Usman, M. and Sani, S. S.(2020). Modelling the Determinants of Under-five Child Mortality Rates Using Cox Proportional Hazards Regression Model. FUDMA Journal of Sciences (FJS), 4(4), 401–408. DOI: https://doi.org/10.33003/fjs.
Nikpour, A., Charati, J. Y., Maleki, I., Ranjbaran, H. and Khalilian, A. (2016). Parametric Model Evaluation in Examining the Survival of Gastric Cancer Patients Andits Influencing Factors. Global Journal of Health Science; 9(3), 260-265. doi:10.5539/gjhs.v9n3p260.
Popoola, A., Ogunleye, O., and Ibrahim, N., (2012). Five Year Survival of Patients with Breast Cancer at the Lagos State University Teaching Hospital, Nigeria. Online Journal Medical and Medical Science, 1, 24-31.
Syahila, E. A., Mohd Asru,l A. A., Sie, L. K. and Siti Afiqah, M. J. (2017). Analysis of Survival in Breast Cancer Patients by Using Different Parametric Models. IOP Conf. Series: Journal of Physics, Conf. Series 890(1), 1-6. DOI: 10.1088/1742-6596/890/1/012169
Teshnizi, S. H. and Ayatollahi, S. M. T. (2017). Comparison of Cox Regression and Parametric Models: Application for Assessment of Survival of Pediatric Cases of Acute Leukemia in Southern Iran. Asian. Pac J Cancer Prev, 18 (4), 981-985.
Vallinayagam, V., Prathap, S, and Venkatesan, P, (2014). Parametric Regression Models in the Analysis of Breast Cancer Survival Data. International Journal of Science and Technology, 3(3).
Zare, N., Doostfatemeh, M. and Rezaianzadeh, A. (2012): Modeling of Breast Cancer Prognostic Factors using a Parametric Log-Logistic Model in Fars province, Southern Iran. Asian Pacific Journal of Cancer Prevention, 13, 1537.
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