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Black Hispanic and Black Non-Hispanic Breast Cancer Survival Data Analysis with Half-normal Model Application

  • Khan, Hafiz Mohammad Rafiqullah (Department of Biostatistics, Robert Stempel College of Public Health & Social Work, Florida International University) ;
  • Saxena, Anshul (Department of Health Promotion & Disease Prevention, Robert Stempel College of Public Health & Social Work, Florida International University) ;
  • Vera, Veronica (Department of Biostatistics, Robert Stempel College of Public Health & Social Work, Florida International University) ;
  • Abdool-Ghany, Faheema (Department of Biostatistics, Robert Stempel College of Public Health & Social Work, Florida International University) ;
  • Gabbidon, Kemesha (Department of Health Promotion & Disease Prevention, Robert Stempel College of Public Health & Social Work, Florida International University) ;
  • Perea, Nancy (Department of Biostatistics, Robert Stempel College of Public Health & Social Work, Florida International University) ;
  • Stewart, Tiffanie Shauna-Jeanne (Department of Dietetics and Nutrition, Robert Stempel College of Public Health & Social Work, Florida International University) ;
  • Ramamoorthy, Venkataraghavan (Department of Dietetics and Nutrition, Robert Stempel College of Public Health & Social Work, Florida International University)
  • Published : 2014.11.28

Abstract

Background: Breast cancer is the second leading cause of cancer death for women in the United States. Differences in survival of breast cancer have been noted among racial and ethnic groups, but the reasons for these disparities remain unclear. This study presents the characteristics and the survival curve of two racial and ethnic groups and evaluates the effects of race on survival times by measuring the lifetime data-based half-normal model. Materials and Methods: The distributions among racial and ethnic groups are compared using female breast cancer patients from nine states in the country all taken from the National Cancer Institute's Surveillance, Epidemiology, and End Results cancer registry. The main end points observed are: age at diagnosis, survival time in months, and marital status. The right skewed half-normal statistical probability model is used to show the differences in the survival times between black Hispanic (BH) and black non-Hispanic (BNH) female breast cancer patients. The Kaplan-Meier and Cox proportional hazard ratio are used to estimate and compare the relative risk of death in two minority groups, BH and BNH. Results: A probability random sample method was used to select representative samples from BNH and BH female breast cancer patients, who were diagnosed during the years of 1973-2009 in the United States. The sample contained 1,000 BNH and 298 BH female breast cancer patients. The median age at diagnosis was 57.75 years among BNH and 54.11 years among BH. The results of the half-normal model showed that the survival times formed positive skewed models with higher variability in BNH compared with BH. The Kaplan-Meir estimate was used to plot the survival curves for cancer patients; this test was positively skewed. The Kaplan-Meier and Cox proportional hazard ratio for survival analysis showed that BNH had a significantly longer survival time as compared to BH which is consistent with the results of the half-normal model. Conclusions: The findings with the proposed model strategy will assist in the healthcare field to measure future outcomes for BH and BNH, given their past history and conditions. These findings may provide an enhanced and improved outlook for the diagnosis and treatment of breast cancer patients in the United States.

Keywords

References

  1. Ademuyiwa FO, Edge SB, Erwin DO, et al (2011). Breast cancer racial disparities: unanswered questions. Cancer Research, 71, 640-44. https://doi.org/10.1158/0008-5472.CAN-10-3021
  2. Akinyemiju TF, Soliman AS, Johnson NJ, et al (2013). Individual and neighborhood socioeconomic status and healthcare resources in relation to Black-white breast cancer survival disparities. J Cancer Epidemiology, 2013, 1-13.
  3. American Cancer Society (ACS) (2014). How many women get breast cancer? Retrieved March 28, 2014, from http://www.cancer.org/cancer/breastcancer/overviewguide/breast-cancer-overview-key-statistics.
  4. ACS (2013a). Cancer Facts and Figures. Retrieved from: http://www.cancer.org/research/cancerfactsstatistics/breast-cancer-facts-figures.
  5. ACS (2013b). Breast Cancer Diagnosis and Pathology. Retrieved from: http://www.cancer.gov/cancertopics/pdq/screening/breast/healthprofessional/page1/AllPages.
  6. Banegas MP, Li CI (2012). Breast cancer characteristics and outcomes among Hispanic Black and Hispanic White women. Breast Cancer Res Treatment, 134, 1297-304. https://doi.org/10.1007/s10549-012-2142-1
  7. Cheung MR (2013). Assessing the impact of socio-economic variables on breast cancer treatment outcome disparity. Asian Pac J Cancer Prev, 14, 7133-6. https://doi.org/10.7314/APJCP.2013.14.12.7133
  8. DeSantis C, Siegel R, Bandi P, Jemal A (2011). Breast cancer statistic, 61, 408-18.
  9. Ferlay J, Soerjomataram I, Ervik M, et al (2012). Cancer incidence and mortality worldwide: IARC CancerBase. GLOBOCAN, 1(11). Lyon, France: international agency for research on cancer; 2013. Retrieved from http://globocan.iarc.fr. Accessed June 2014.
  10. IBM Corp. (2010). SPSS Statistics for Windows, Version 19.0. Armonk, NY.
  11. Intercultural Cancer Council (ICC) (2012). Hispanics/Latinos and Cancer. Retrieved April 20, 2014, from http://www.iccnetwork.org/cancerfacts/HispFactSheetJune2011RevPost.pdf.
  12. Jemal A, Bray F, Center MM, et al (2011). Global cancer statistics. CA, 61, 69-90.
  13. Jemal A, Siegel R, Ward E, et al (2006). Cancer Statistics, 2006. CA: a cancer journal for clinicians, 56, 106-30. https://doi.org/10.3322/canjclin.56.2.106
  14. Kaiser K, Rauscher GH, Jacobs EA, et al (2011). The import of trust in regular providers to trust in cancer physicians among white, African American, and Hispanic breast cancer patients. J General Intern Med, 26, 51-7. https://doi.org/10.1007/s11606-010-1489-4
  15. Khan HMR (2012). Predictive inference for a future response using symmetrically trimmed sample from the half-normal model. Computational Statist Data Analysis, 56, 1350-61. https://doi.org/10.1016/j.csda.2011.10.009
  16. Khan HMR (2013). Predictive inference from the half-normal model given a type II censored sample, Communications in Statistics. Theory Methods, 42, 42-55. https://doi.org/10.1080/03610926.2011.581789
  17. Khan HMR, Saxena A, Rana S, Ahmed NU (2014a). Bayesian modeling for male breast cancer data. Asian Pac J Cancer Prev, 15, 663-9. https://doi.org/10.7314/APJCP.2014.15.2.663
  18. Khan HMR, Saxena A, Gabbidon K, Rana S, Ahmed NU (2014b). Model-based survival estimates of female breast cancer data. Asian Pac J Cancer Prev, 15, 2893-900. https://doi.org/10.7314/APJCP.2014.15.6.2893
  19. Khan HMR, Saxena A, Shrestha A (2014c). Posterior inference for white Hispanic breast cancer survival data. J Biomet Biostat, 5, 183.
  20. Khan HMR, Saxena A, Gabbidon K, Stewart TS, Bhatt C (2014d). Survival analysis for white non-Hispanic female breast cancer patients. Asian Pac J Cancer Prev, 15, 4049-54. https://doi.org/10.7314/APJCP.2014.15.9.4049
  21. Khan HMR, Saxena A, Ross E, Ramamoorthy V, Sheehan D (2014e). Inferential statistics from black hispanic breast cancer survival data. Scientific World Journal, 2014, 13.
  22. Khan HMR, Saxena A, Gabbidon K, Ross E, Shrestha A (2014f). Statistical applications for the prediction of white Hispanic breast cancer survival data. Asian Pac J Cancer Prev, 15, 5571-5. https://doi.org/10.7314/APJCP.2014.15.14.5571
  23. Levy DE, Byfield SD, Comstock CB, et al (2011). Underutilization of BRCA1/2 testing to guide breast cancer treatment: Black and Hispanic women particularly at risk.Genetics Med, 13, 349-55. https://doi.org/10.1097/GIM.0b013e3182091ba4
  24. Liu L, Zhang J, Wu AH, Pike MC, Deapen D (2012). Invasive breast cancer incidence trends by detailed race/ethnicity and age. Intern J Cancer, 130, 395-404. https://doi.org/10.1002/ijc.26004
  25. Memon ZA, Shaikh AN, Rizwan S, Sardar MB (2013). Reasons for patient's delay in diagnosis of breast carcinoma in Pakistan. Asian Pac J Cancer Prev, 14, 7409-14. https://doi.org/10.7314/APJCP.2013.14.12.7409
  26. Narod SA (2011). Hormone replacement therapy and the risk of breast cancer. Nature Rev Clin Oncol, 8, 669-76. https://doi.org/10.1038/nrclinonc.2011.110
  27. National Cancer Institute (NCI) (2014a). Breast Cancer. Retrieved March 25, 2014, from http://www.cancer.gov/cancertopics/types/breast.
  28. NCI (2014b). What You Need To Know $About^{TM}$. Retrieved March 28, 2014, from http://www.cancer.gov/cancertopics/wyntk/breast.
  29. Ooi SL, Martinez ME, Li CI (2011). Disparities in breast cancer characteristics and outcomes by race/ethnicity. Breast cancer research and treatment, 127, 729-38. https://doi.org/10.1007/s10549-010-1191-6
  30. Sankaranarayanan R, Ferlay J (2013). Burden of breast and gynecological cancers in low-resource countries. in breast and gynecological cancers (pp.1-17). Springer New York.
  31. SAS Institute Inc. (2011). SAS$^{(R)}$ software, Version 9.3 of the SAS System for Windows, NC, USA
  32. SEER (2014a). SEER cancer statistics factsheets: breast cancer. national cancer institute. Bethesda, MD.
  33. SEER (2014b). SEER Training Modules. SEER Training:introduction to breast cancer. Retrieved April 20, 2014, from http://training.seer.cancer.gov/breast/intro/.
  34. Siegel R, DeSantis C, Virgo K, et al (2012b). Cancer treatment and survivorship statistics, 2012. CA: Cancer J Clin, 62, 220-41. https://doi.org/10.3322/caac.21149
  35. Siegel R, Naishadham D, Jemal A (2012a). Cancer statistics for hispanics/latinos, 2012. CA: Cancer J Clin, 62, 283-98. https://doi.org/10.3322/caac.21153
  36. Siegel R, Ma J, Zou Z, Jemal A (2014). Cancer statistics, 2014. CA Clin, 64, 9-29. https://doi.org/10.3322/caac.21208
  37. Smith RA, Cokkinides V, Brooks D, et al (2011). Cancer screening in the United States, 2011. CA: Cancer J Clin, 61, 8-30. https://doi.org/10.3322/caac.20096
  38. Trapido EJ, Chen F, Davis K, et al (1994) Cancer in south Florida hispanic women. A 9-year assessment. Arch Intern Med, 154, 1083-8. https://doi.org/10.1001/archinte.1994.00420100051008
  39. Trapido EJ, McCoy CB, Stein NS, et al (1990). The epidemiology of cancer among hispanic women. the experience in Florida. Cancer, 66, 2435-41. https://doi.org/10.1002/1097-0142(19901201)66:11<2435::AID-CNCR2820661133>3.0.CO;2-0
  40. Van de Water W, Markopoulos C, van de Velde CJ, et al (2012). Association between age at diagnosis and disease-specific mortality among postmenopausal women with hormone receptor-positive breast cancer. JAMA, 307, 590-97.
  41. Virnig BA, Tuttle TM, Shamliyan T, Kane RL (2010). Ductal carcinoma in situ of the breast: a systematic review of incidence, treatment, and outcomes. J Nat Cancer Inst, 102, 170-8. https://doi.org/10.1093/jnci/djp482
  42. WHO. Charts for the 10 leading causes of death for women worldwide and by income group, Geneva: WHO, 2013. Retrieved from http://www.who.int/nmh/publications/ncd_report_full_en.pdf. Accessed June 2014.
  43. WHO. World health statistics. World Health Organization, 2010. Retrieved from http://www.who.int/gho/publications/world_health_statistics/EN_WHS10_Full.pdf?ua=1, Accessed June 2014.
  44. Wolfram Research (2012). The Mathematica Archive: Mathematica 8.0. Wolfram Research Inc, Illinois.

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