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Statistical Estimates from Black Non-Hispanic Female Breast Cancer Data

  • Khan, Hafiz Mohammad Rafiqullah (Department of Biostatistics, Robert Stempel College of Public Health and Social Work, Florida International University) ;
  • Ibrahimou, Boubakari (Department of Biostatistics, Robert Stempel College of Public Health and Social Work, Florida International University) ;
  • Saxena, Anshul (Department of Health Promotion and Disease Prevention, Robert Stempel College of Public Health and Social Work, Florida International University) ;
  • Gabbidon, Kemesha (Department of Health Promotion and Disease Prevention, Robert Stempel College of Public Health and Social Work, Florida International University) ;
  • Abdool-Ghany, Faheema (Department of Biostatistics, Robert Stempel College of Public Health and Social Work, Florida International University) ;
  • Ramamoorthy, Venkataraghavan (Department of Dietetics and Nutrition, Robert Stempel College of Public Health and Social Work, Florida International University) ;
  • Ullah, Duff (Department of Biostatistics, Robert Stempel College of Public Health and Social Work, Florida International University) ;
  • Stewart, Tiffanie Shauna-Jeanne (Department of Dietetics and Nutrition, Robert Stempel College of Public Health and Social Work, Florida International University)
  • Published : 2014.10.23

Abstract

Background: The use of statistical methods has become an imperative tool in breast cancer survival data analysis. The purpose of this study was to develop the best statistical probability model using the Bayesian method to predict future survival times for the black non-Hispanic female breast cancer patients diagnosed during 1973-2009 in the U.S. Materials and Methods: We used a stratified random sample of black non-Hispanic female breast cancer patient data from the Surveillance Epidemiology and End Results (SEER) database. Survival analysis was performed using Kaplan-Meier and Cox proportional regression methods. Four advanced types of statistical models, Exponentiated Exponential (EE), Beta Generalized Exponential (BGE), Exponentiated Weibull (EW), and Beta Inverse Weibull (BIW) were utilized for data analysis. The statistical model building criteria, Akaike Information Criteria (AIC), Bayesian Information Criteria (BIC), and Deviance Information Criteria (DIC) were used to measure the goodness of fit tests. Furthermore, we used the Bayesian approach to obtain the predictive survival inferences from the best-fit data based on the exponentiated Weibull model. Results: We identified the highest number of black non-Hispanic female breast cancer patients in Michigan and the lowest in Hawaii. The mean (SD), of age at diagnosis (years) was 58.3 (14.43). The mean (SD), of survival time (months) for black non-Hispanic females was 66.8 (30.20). Non-Hispanic blacks had a significantly increased risk of death compared to Black Hispanics (Hazard ratio: 1.96, 95%CI: 1.51-2.54). Compared to other statistical probability models, we found that the exponentiated Weibull model better fits for the survival times. By making use of the Bayesian method predictive inferences for future survival times were obtained. Conclusions: These findings will be of great significance in determining appropriate treatment plans and health-care cost allocation. Furthermore, the same approach should contribute to build future predictive models for any health related diseases.

Keywords

Model development;Bayesian method;statistical inference;breast cancer survival;black non-Hispanic

References

  1. Graeser MK, Engel C, Rhiem K, et al (2009). Contralateral breast cancer risk in BRCA1 and BRCA2 mutation carriers. J Clinical Oncol, 27, 5887-92. https://doi.org/10.1200/JCO.2008.19.9430
  2. ACS (2013). Cancer Facts and Figures. Atlanta: American Cancer Society.
  3. Alteri R, Bandi P, Brinton L, Casares C, Cokkinides V, Gansler T. Breast cancer facts and figures 2011-2012. American Cancer Society, Atlanta, 2011.
  4. Bradbury AR, Olopade OI (2007). Genetic susceptibility to breast cancer. Reviews Endocrine Metabolic Disorders, 8, 255-67. https://doi.org/10.1007/s11154-007-9038-0
  5. DeSantis C, Siegel R, Bandi P, Jemal A (2011) Breast cancer statistics, CA, 61, 408-18. https://doi.org/10.1017/S0009840X11000941
  6. Ferlay J, Soerjomataram I, Ervik M, et al (2012). Cancer incidence and mortality worldwide: IARC Cancer Base. GLOBOCAN, 1(11). Lyon, France: International agency for research on cancer; 2013.
  7. Jemal A, Bray F, Center MM, et al (2011). Global cancer statistics. CA, 61, 69-90.
  8. Khan HMR, Saxena A, Shrestha A (2014). Posterior inference for the white Hispanic breast cancer survival data. J Biomet Biostat, 5, 1-6.
  9. Khan HMR, Saxena A, Rana S, Ahmed NU (2014). Bayesian modeling for male breast cancer data. Asian Pac J Cancer Prev, 15, 663-69. https://doi.org/10.7314/APJCP.2014.15.2.663
  10. Khan HMR, Saxena A, Kemesha G, Rana S, Ahmed NU (2014). Mode-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
  11. Khan HM, 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
  12. Khan HMR (2012). Estimating predictive inference for responses from the generalized Rayleigh model based on complete sample. J Thai Statistician, 10, 53-68.
  13. Khan HMR .( 2012) Several priors based inference from the exponential model under censoring. JP J Fund Appl Stat, 2, 1-13.
  14. Khan HMR, Haq MS, Provost SB (2004). Predictive distributions for responses from the Weibull life-testing model. J Stat Theory Applications, 3, 53-73.
  15. Khan HMR (2013). Comparing relative efficiency from a right skewed model. JP J Biostat, 9, 1-26.
  16. Khan HMR (2013). Inferential estimates from the one-parameter half-normal model. J Thai Statistician, 11, 77-95.
  17. Khan HMR, Albatineh AN, Alshahrani S, Jenkins N, Ahmed NU (2011) Sensitivity analysis of predictive modeling for responses from the three-parameter Weibull model with a follow-up doubly censored sample of cancer patients. Computational Statistics Data Analysis, 55, 3093-103. https://doi.org/10.1016/j.csda.2011.05.017
  18. Kwan ML, Kushi LH, Weltzien E, et al (2010). Alcohol consumption and breast cancer recurrence and survival among women with early-stage breast cancer: the life after cancer epidemiology study. J Clin Oncol, 28, 4410-6. https://doi.org/10.1200/JCO.2010.29.2730
  19. Liu L, Zhang J, Wu AH, Pike MC, Deapen D (2012). Invasive breast cancer incidence trends by detailed race/ethnicity and age. Int J Cancer, 130, 395-404. https://doi.org/10.1002/ijc.26004
  20. Mavaddat N, Antoniou AC, Easton DF, Garcia-Closas M (2010). Genetic susceptibility to breast cancer. Molecular Oncology. 4, 174-91. https://doi.org/10.1016/j.molonc.2010.04.011
  21. Peairs KS, Barone BB, Snyder CF, et al (2011). Diabetes mellitus and breast cancer outcomes: a systematic review and metaanalysis. J Clin Oncol, 29, 40-6. https://doi.org/10.1200/JCO.2009.27.3011
  22. Protani M, Coory M, Martin JH (2010). Effect of obesity on survival of women with breast cancer: systematic review and meta-analysis. Br Ca Res Treat, 123, 627-35. https://doi.org/10.1007/s10549-010-0990-0
  23. Smith RA, Brooks D, Cokkinides V, Saslow D, Brawley OW (2013). Cancer screening in the United States, 2013: a review of current American Cancer Society guidelines, current issues in cancer screening, and new guidance on cervical cancer screening and lung cancer screening. CA, 63, 88-105. https://doi.org/10.1017/S0009840X1200248X
  24. Robertson A, Ragupathy SK, Boachie C, et al (2011). The clinical effectiveness and costeffectiveness of different surveillance mammography regimens after the treatment forprimary breast cancer: systematic reviews registry database analyses and economic evaluation. Health Technology Assessment, 15, 1-322.
  25. Sankaranarayanan R, Ferlay J (2013). Burden of breast and gynecological cancers in low-resource countries. Breast and Gynecological Cancers, 1-17.
  26. Siegel R, DeSantis C, Virgo K, et al (2012). Cancer treatment and survivorship statistics. CA, 62, 220-41.
  27. Sprague BL, Trentham-Dietz A, Gangnon RE, et al (2011). Socioeconomic status and survival after an invasive breast cancer diagnosis. Cancer, 117, 1542-51. https://doi.org/10.1002/cncr.25589
  28. Surveillance, Epidemiology and End Results (SEER). Cancer of the Breast - SEER Stat Fact Sheets.
  29. Walker MJ, Mirea L, Cooper K, et al (2013). Impact of familial risk and mammography screening on prognostic indicators of breast disease among women from the Ontario site of the Breast Cancer Family Registry. Familial Cancer, 13, 163-72.
  30. WHO (2013). Charts for the 10 leading causes of death for women worldwide and by income group, Geneva. Retrieved from http://www.who.int/nmh/publications/ncd_report_full_en.pdf. Accessed January 2014.
  31. WHO (2010). World health statistics. Retrieved from http://www.who.int/gho/publications/world_health_statistics/EN_WHS10_Full.pdf?ua=1, Accessed January 2014.