Survival analysis for white non-hispanic female breast cancer patients

Asian Pacific Journal of Cancer Prevention

Volumn 15, Issue 9, 2014, Pages 4049-4054

  Khan, Hafiz Mohammad Rafiqullah ^a   Saxena, Anshul ^a   Gabbidon, Kemesha ^a   Stewart, Tiffanie Shauna Jeanne ^a   Bhatt, Chintan ^a  

^a FLORIDA INTERNATIONAL UNIVERSITY   (United States)

Author keywords

Bayesian inference; Breast cancer survival data; Predictive inference; Statistical probability models

Indexed keywords

BAYES THEOREM; BREAST TUMOR; CANCER REGISTRY; CANCER STAGING; CAUCASIAN; FEMALE; HUMAN; MIDDLE AGED; MORTALITY; PROGNOSIS; RANDOMIZATION; STATISTICAL MODEL; SURVIVAL; SURVIVAL RATE; UNITED STATES;

BAYES THEOREM; BREAST NEOPLASMS; EUROPEAN CONTINENTAL ANCESTRY GROUP; FEMALE; HUMANS; MIDDLE AGED; MODELS, STATISTICAL; NEOPLASM STAGING; PROGNOSIS; RANDOM ALLOCATION; SEER PROGRAM; SURVIVAL ANALYSIS; SURVIVAL RATE; UNITED STATES;

EID: 84902688296     PISSN: 15137368     EISSN: 2476762X     Source Type: Journal    
DOI: 10.7314/APJCP.2014.15.9.4049     Document Type: Article

References (36)

1. ACS (2013). Cancer Facts and Figures. Atlanta: American Cancer Society. Retrieved from: http://www.cancer.org/research/cancerfactsstatistics/breast-cancer-facts-figures. [Accessed September 15, 2013]
2. Barreto-Souza W, Santos AHS, Cordeiro GM (2010). The beta generalized exponential distribution. J Stat Comput Sim, 80, 159-72.
3. CDC (2012). Breast Cancer Rates by Race and Ethnicity. Available at: http://www.cdc.gov/cancer/breast/statistics/race.htm. Accessed June 12, 2012.
4. Clarke CA, Keegan THM, Yang J, et al (2012). Age-specific incidence of breast cancer subtypes: understanding the black-white crossover. J Natl Cancer Inst, 104, 1094-101.
5. DeSantis C, Siegel R, Bandi P, Jemal A (2011). Breast cancer statistics. CA Cancer J Clin, 61, 409-18.
6. Ferlay J, Soerjomataram I, Ervik M, et al (2013). Cancer Incidence and Mortality Worldwide: IARC Cancer Base. GLOBOCAN 2012, 1 (11). Lyon, France: International Agency for Research on Cancer. Available from: http://globocan.iarc.fr, [Accessed December 20, 2013].
7. Gonzalez H, Halevy AY, Jensen CS, et al (2010). Google fusion tables: web-centered data management and collaboration. In Proceedings of the 2010 ACM SIGMOD International Conference on Management of data (pp. 1061-66). New York, NY, USA: ACM.
8. Graeser MK, Engel C, Rhiem K, et al (2009). Contralateral breast cancer risk in BRCA1 and BRCA2 mutation carriers. J Clin Oncol, 27, 5887-92.
9. Hou N, Huo D (2013). A trend analysis of breast cancer incidence rates in the United States from 2000 to 2009 shows a recent increase. Breast Cancer Res Treat, 138, 633-41.
10. IBM SPSS (2011). IBM SPSS Statistics for Windows, Version 20.0. Armonk, NY: IBM Corp.
11. Jemal A, Bray F, Center MM, et al (2011). Global cancer statistics. CA Cancer J Clin, 61, 69-90.
12. Khan HMR, Saxena A, Shrestha A (2014a). Posterior inference for White Hispanic breast cancer survival data. J Biomet Biostat, 5, 183.
13. Khan HMR, Saxena A, Rana S, Ahmed NU (2014b). Bayesian modeling for male breast cancer data. Asian Pac J Cancer Prev, 15, 663-9.
14. Khan HMR, Saxena A, Kemesha G, Rana S, Ahmed NU (2014c). Mode-based survival estimates of female breast cancer data. Asian Pac J Cancer Prev, 15, 2893-900.
15. Khan HMR, Saxena A, Kemesha G, Elizabeth R, Alice S (2014d). Statistical applications for White Hispanic breast cancer survival data. Asian Pac J Cancer Prev, [Epub ahead of print].
16. Khan HMR (2012a). Estimating predictive inference for responses from the generalized Rayleigh model based on complete sample. J Thai Statistician, 10, 53-68.
17. Khan HMR (2012b). Several priors based inference from the exponential model under censoring. JP J Fund Appl Stat, 2, 1-13.
18. Khan HMR (2013a). Comparing relative efficiency from a right skewed model. JP J Biostat, 9, 1-26.
19. Khan HMR (2013b). Inferential estimates from the one-parameter half-normal model. J Thai Statistician, 11, 77-95.
20. 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.
21. 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.
22. Mariotto AB, Yabroff KR, Shao Y, Feuer EJ, Brown ML (2011). Projections of the cost of cancer care in the United States: 2010-2020. J Natl Cancer Inst, 103, 117-28.
23. Montero AJ, Eapen S, Gorin B, Adler P (2012). The economic burden of metastatic breast cancer: a U.S. managed care perspective. Breast Cancer Res Treat, 134, 815-22.
24. MRC Biostatistics Unit (2013). WinBUGS software version 1.4.3: The BUGS Project.
25. Peairs KS, Barone BB, Snyder CF, et al (2011). Diabetes mellitus and breast cancer outcomes: a systematic review and meta-analysis. J Clin Oncol, 29, 40-6.
26. Protani M, Coory M, Martin JH (2010). Effect of obesity on survival of women with breast cancer: systematic review and meta-analysis. Breast Cancer Res Treat, 123, 627-35.
27. Robertson A, Ragupathy SK, Boachie C, et al (2011). The clinical effectiveness and cost-effectiveness of different surveillance mammography regimens after the treatment for primary breast cancer: systematic reviews registry database analyses and economic evaluation. Health Technol Assess, 15, 1-322.
28. SEER. (2010). Surveillance, Epidemiology, and End Results (SEER) Program (www.seer.cancer.gov) Research Data (1973-2009), National Cancer Institute, DCCPS, Surveillance Research Program. [Accessed July 10, 2013].
29. Siegel R, Ma J, Zou Z, Jemal A (2014). Cancer statistics, 2014. CA Cancer J Clin, 64, 9-29.
30. 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 Cancer J Clin, 63, 88-105.
31. Sprague BL, Trentham-Dietz A, Gangnon RE, et al (2011). Socioeconomic status and survival after an invasive breast cancer diagnosis. Cancer, 117, 1542-51.
32. 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. Fam Cancer, 13, 163-72.
33. White A, Richardson LC, Krontiras H, Pisu M (2013). Socioeconomic disparities in breast cancer treatment among older women. J Womens Health (Larchmt), 23, 335-41.
34. WHO (2013a). Cause-specific mortality and morbidity: age-standardized mortality rate by cause by World Bank income group. Geneva: WHO.
35. WHO (2013b). Charts for the 10 leading causes of death for women worldwide and by income group. Geneva: WHO.
36. Wolfram Research (2012). The Mathematica Archive: Mathematica 8.0.