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Journal of Applied Sciences

Year: 2011 | Volume: 11 | Issue: 6 | Page No.: 1033-1038
Research Article

Identify Attributable Variables and Interactions in Breast Cancer

Yong Xu
Department of Mathematics and Statistics, Radford University, VA, 24142, USA
James Kepner
250 Williams Street, Suite 600, American Cancer Society, Atlanta, GA 30303, USA
Chris P. Tsokos
Department of Mathematics and Statistics, Radford University, VA, 24142, USA
The object of the present study is to develop a statistical model for breast cancer tumor size prediction for United States patients based on real uncensored data. When we simulate breast cancer tumor size, most of time these tumor sizes are randomly generated. We want to construct a statistical model to generate these tumor sizes as close as possible to the real patients’ data given other related information. We accomplish the objective by developing a high quality statistical model that identifies the significant attributable variables and interactions. We rank these contributing entities according to their percentage contribution to breast cancer tumor growth. This proposed statistical model can also be used to conduct surface response analysis to identify the necessary restrictions on the significant attributable variables and their interactions to minimize the size of the breast tumor.
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References

  1. Allen, D.M., 1971. The prediction sum of squares as a criterion for selecting predictor variables. Technical Report No. 23, Department of Statistics, University of Kentucky.
  2. Allen, D.M., 1974. The relationship between variable selection and data augmentation and a method for prediction. Technometrics, 16: 125-127.
    Direct Link
  3. Box, G.E.P. and D.R. Cox, 1964. An analysis of transformation. J. Roy. Stat. Soci., 26: 211-252.
    Direct Link
  4. Brawley, O.W., 2009. Is race really a negative prognostic factor for cancer. J. Nat. Cancer Instit., 101: 970-971.
    CrossRefDirect Link
  5. Eleni, A. and N.H. Gabriel, 2008. Prognostic factors in metastatic breast cancer successes and challenges toward individualized therapy. J. Clin. Oncol., 26: 3360-3662.
    CrossRefDirect Link
  6. Freedman, R.A., Y. He, E.P. Winer and N.L. Keating, 2009. Trends in racial and age disparities in definitive local therapy of early-stage breast cancer. J. Clin. Oncol., 27: 713-719.
    CrossRefDirect Link
  7. Habibi, G., S. Leung, J.H. Law, K. Gelmon and H. Masoudi et al., 2008. Redefining prognostic factors for breast cancer: YB-1 is a stronger predictor of relapse and disease-specific survival than estrogen receptor or HER-2 across all tumor subtyples. BCR., 10: 86-86.
    CrossRefDirect Link
  8. Ibrahim, N.A., A. Kudus, I. Daud and M.R. Abu-Bakar, 2008. Decision tree for competing risks survival probability in breast cancer study. World Acad. Sci. Eng. Technol., 38: 15-19.
    Direct Link
  9. National Cancer Institue, 2010. Breast cancer. http://www.cancer.gov/cancertopics/types/breast.
  10. Qiu, P. and C.P. Tsokos, 2000. Accelerated life-testing model building with Box-Cox transformation. Sankhya Idian J. Stat., 62: 223-235.
    Direct Link
  11. Shang, L.C. and T.M.G. Jeremy, 2002. A semi-parametric accelerated failure time cure model. Stat. Med., 21: 3235-3247.
    Direct Link
  12. Shapiro, S.S. and M.B. Wilk, 1965. An analysis of variance test for normality (Complete samples). Biometrika, 52: 591-611.
    CrossRefDirect Link
  13. Uddin, M.T., M.N. Islam and Q.I.U. Ibrahim, 2006. An analytical approach on cure rate estimation based on uncensored data. J. Applied Sci., 6: 548-552.
    CrossRefDirect Link

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