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 ABCR  Vol.6 No.1 , January 2017
Competing Risks Analysis of African American Breast Cancer Patients
Abstract: Purpose: Recent studies showed that African Americans (AA) breast cancer patients experience lower survival than any other race. The knowledge of cause-specific survival of such patients is necessary to investigate the different factors associated with the disease and support the clinical practice. Methods: The parametric competing risk method is applied to build up the survival models and the parametric mixture model is used to study the overall survival of these patients. The Kaplan-Meier survival estimation is also computed to compare the results. Results: The overall death rate decreases sharply immediately after the diagnosis and increases thereafter. The risk of death from breast cancer itself is the highest at the first five years; other causes, however, pose more threats to patients after this period. The patients who received only surgery have higher survival rate in long run. The use of radiation only does not have the significant effect on patients’ survival. Conclusion: Our study shows that the parametric competing risk models are promising in estimating the cause-specific survival of AA breast cancer patients and can be used for clinical practice. We also observed that heart and other diseases pose more threat to breast cancer patients in the long run.
Cite this paper: Pham, M. and Kafle, R. (2017) Competing Risks Analysis of African American Breast Cancer Patients. Advances in Breast Cancer Research, 6, 28-41. doi: 10.4236/abcr.2017.61003.
References

[1]   Ferlay, J., Soerjomataram, I., Ervik, M., Dikshit, R., Eser, S., Mathers, C., Rebelo, M., Parkin, D.M., Forman, D. and Bray, F. (2013) GLOBOCAN 2012 v1.0, Cancer Incidence and Mortality Worldwide: IARC CancerBase No. 11 [Internet]. International Agency for Research on Cancer, Lyon.

[2]   DeSantis, C., Ma, J., Bryan, L. and Jemal, A, (2014) Breast Cancer Statistics, 2013. CA Caner Journal for Clinicians, 64, 52-62.
https://doi.org/10.3322/caac.21203

[3]   DeSantis, C., Siegel, R., Bandi, P. and Jemal, A. (2011) Breast Cancer Statistics, 2011. CA Caner Journal for Clinicians, 61, 409-418.
https://doi.org/10.3322/caac.20134

[4]   National Cancer Institute (2014) Cancer of the Breast—SEER Stat Fact Sheets [Internet]. Surveillance, Epidemiology, and End Results Program.
http://seer.cancer.gov/statfacts/html/breast.html

[5]   Smigal, C., Jemal, A., Ward, E., Cokkinides, V., Smith, R., Howe, H.L., et al. (2006) Trends in Breast Cancer by Race and Ethnicity: Update 2006. CA: A Cancer Journal for Clinicians, 56, 168-183.
https://doi.org/10.3322/canjclin.56.3.168

[6]   Ashing-Giwa, K.T., Padilla, G., Tejero, J., Kraemer, J., Wright, K., Coscarelli, A., et al. (2004) Understanding the Breast Cancer Experience of Women: A Qualitative Study of African American, Asian American, Latina and Caucasian Cancer Survivors. Psycho-Oncology, 13, 408-428.
https://doi.org/10.1002/pon.750

[7]   Clegg, L.X., Reichman, M.E., Miller, B.A., Hankey, B.F., Singh, G.K., Lin, Y.D., et al. (2009) Impact of Socioeconomic Status on Cancer Incidence and Stage at Diagnosis: Selected Findings from the Surveillance, Epidemiology, and End Results: National Longitudinal Mortality Study. Cancer Causes Control, 20, 417-435.
https://doi.org/10.1007/s10552-008-9256-0

[8]   Wei, L.J. (1992) The Accelerated Failure Time Model: A Useful Alternative to the Cox Regression Model in Survival Analysis. Statistics in Medicine, 11, 1871-1879.
https://doi.org/10.1002/sim.4780111409

[9]   Strasser-Weippl, K. and Goss, P.E. (2013) Competing Risks in Low-Risk Breast Cancer. American Society of Clinical Oncology Educational Book, 32-39.
https://doi.org/10.1200/EdBook_AM.2013.33.32

[10]   Rosenberg, M.A. (2006) Competing Risks to Breast Cancer Mortality. JNCI Monographs, 36, 15-19.
https://doi.org/10.1093/jncimonographs/lgj004

[11]   Lin, D.Y. (1997) Non-Parametric Inference for Cumulative Incidence Functions in Competing Risks Studies. Statistics in Medicine, 16, 901-910.
https://doi.org/10.1002/(SICI)1097-0258(19970430)16:8<901::AID-SIM543>3.0.CO;2-M

[12]   Dignam, J.J., Zhang, Q. and Kocherginsky, M. (2012) The Use and Interpretation of Competing Risks Regression Models. Clinical Cancer Research, 18, 2301-2308.
https://doi.org/10.1158/1078-0432.CCR-11-2097

[13]   McGiffin, D.C., Naftel, D.C., Kirklin, J.K., Morrow, W.R., Towbin, J., Shaddy, R., et al. (1997) Predicting Outcome after Listing for Heart Transplantation in Children: Comparison of Kaplan-Meier and Parametric Competing Risk Analysis. The Journal of Heart and Lung Transplantation, 16, 713-722.

[14]   National Cancer Institute (2015) Surveillance, Epidemiology, and End Results (SEER) Program. Research Data (1973-2012), National Cancer Institute, DCCPS, Surveillance Research Program, Surveillance Systems Branch. www.seer.cancer.gov

[15]   National Cancer Institute (NCI) (2016) Surveillance, Epidemiology, and End Results Program.
http://seer.cancer.gov.

[16]   Noone, A.M., Lund, J.L., Mariotto, A., Cronin, K., McNeel, T., Deapen, D. and Warren, J.L. (2016) Comparison of SEER Treatment Data with Medicare Claims. Medical Care, 54, e55-e64.
https://doi.org/10.1097/mlr.0000000000000073

[17]   Warren, J.L., Harlan, L.C., Fahey, A., Virnig, B.A., Freeman, J.L., Klabunde, C.N., et al. (2002) Utility of the SEER-Medicare Data to Identify Chemotherapy Use. Medical Care, 40, IV-55-IV-61.
https://doi.org/10.1097/00005650-200208001-00008

[18]   Prentice, R.L., Kalbfleisch, J.D., Peterson Jr., A.V., Flournoy, N., Farewell, V.T. and Breslow, N.E. (1978) The Analysis of Failure Times in the Presence of Competing Risks. Biometrics, 34, 541-554.
https://doi.org/10.2307/2530374

[19]   Kalbfleisch, J.D. and Prentice, R.L. (1980) The Statistical Analysis of Failure Time Data. Wiley, New York.

[20]   Maller, R.A. and Zhou, X. (2002) Analysis of Parametric Models for Competing Risks. Statistica Sinica, 12, 725-750.

[21]   Jogi, A., Vaapil, M., Johansson, M. and Pahlman, S. (2012) Cancer Cell Differentiation Heterogeneity and Aggressive Behavior in Solid Tumors. Upsala Journal of Medical Sciences, 117, 217-224.
https://doi.org/10.3109/03009734.2012.659294

[22]   Keegan, T.H., DeRouen, M.C., Press, D.J., Kurian, A.W. and Clarke, C.A. (2012) Occurrence of Breast Cancer Subtypes in Adolescent and Young Adult Women. Breast Cancer Research, 14, R55.
https://doi.org/10.1186/bcr3156

[23]   Colzani, E., Liljegren, A., Johansson, A.L., Adolfsson, J., Hellborg, H., Hall, P.F. and Czene, K. (2011) Prognosis of Patients with Breast Cancer: Causes of Death and Effects of Time since Diagnosis, Age, and Tumor Characteristics. Journal of Clinical Oncology, 29, 4014-4021.
https://doi.org/10.1200/JCO.2010.32.6462

[24]   Alison, P.D. Macro Smooth. Statistical Horizons 2010.
http://www.statisticalhorizons.com/resources/macros

[25]   Beaver, K., Luker, K.A., Owens, R.G., Leinster, S.J., Degner, L.F. and Sloan, J.A. (1996) Treatment Decision Making in Women Newly Diagnosed with Breast Cancer. Cancer Nursing, 19, 8-19.
https://doi.org/10.1097/00002820-199602000-00002

[26]   Kantorová, I., Lipská, L., Bêlohlávek, O., Visokai, V., Trubac, M. and Schneiderová, M. (2003) Routine 18F-FDG PET Preoperative Staging of Colorectal Cancer: Comparison with Conventional Staging and Its Impact on Treatment Decision Making. Journal of Nuclear Medicine, 44, 1784-1788.

[27]   Valanis, B.G. and Rumpler, C.H. (1985) Helping Women to Choose Breast Cancer Treatment Alternatives. Cancer Nursing, 8, 167-176.
https://doi.org/10.1097/00002820-198506000-00005

[28]   Shumay, D.M., Maskarinec, G., Kakai, H. and Gotay, C.C. (2001) Why Some Cancer Patients Choose Complementary and Alternative Medicine Instead of Conventional Treatment. Journal of Family Practice, 50, 1067.

[29]   Campbell, J.D. and Ramsey, S.D. (2009) The Costs of Treating Breast Cancer in the US. Pharmacoeconomics, 27, 199-209.
https://doi.org/10.2165/00019053-200927030-00003

[30]   Clark, R.M., McCulloch, P.B., Levine, M.N., Lipa, M., Wilkinson, R.H., Mahoney, L. J., Corbett, P.J., et al. (1992) Randomized Clinical Trial to Assess the Effectiveness of Breast Irradiation Following Lumpectomy and Axillary Disection for Node-Negative Breast Cancer. Journal of the National Cancer Institute, 84, 683-689.
https://doi.org/10.1093/jnci/84.9.683

[31]   Whelan, T.J., Julian, J., Wright, J., Jadad, A.R. and Levine, M.L. (2000) Does Locoregional Radiation Therapy Improve Survival in Breast Cancer? A Meta-Analysis. Journal of Clinical Oncology, 18, 1220-1229.

[32]   Steward, L.T., Gao, F., Taylor, M.A. and Margenthaler, J.A. (2014) Impact of Radiation Therapy on Survival in Patients with Triple-Negative Breast Cancer. Oncology Letters, 7, 548-552.

 
 
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