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Parametric and Nonparametric Analysis of Breast Cancer Treatments

Authors: Chunling Cong, Chris.P.Tsokos

Abstract:

The objective of the present research manuscript is to perform parametric, nonparametric, and decision tree analysis to evaluate two treatments that are being used for breast cancer patients. Our study is based on utilizing real data which was initially used in “Tamoxifen with or without breast irradiation in women of 50 years of age or older with early breast cancer" [1], and the data is supplied to us by N.A. Ibrahim “Decision tree for competing risks survival probability in breast cancer study" [2]. We agree upon certain aspects of our findings with the published results. However, in this manuscript, we focus on relapse time of breast cancer patients instead of survival time and parametric analysis instead of semi-parametric decision tree analysis is applied to provide more precise recommendations of effectiveness of the two treatments with respect to reoccurrence of breast cancer.

Keywords: Decision Tree, non-parametric analysis, breast cancer treatments, parametricanalysis

Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1054767

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References:


[1] A. W. Fyles, D.R. McCready, et al., "Tamoxifen with or without breast irradiation in women 50 years of age or older with early breast cancer", New England Journal of Medicine 351, pp. 963-970, 2004.
[2] N.A Ibrahim, et al "Decision tree for competing risks survival probability in breast cancer study", International Journal of Biomedical Sciences, Volume 3 Number 1, 2008.
[3] Kaplan, E.L.; Meier, Paul. "Nonparametric estimation from incomplete observations". J. Am. Stat. Assoc. 53, 457-481, 1958.
[4] L. Breiman, J. Friedman, R. Olshen, and C. Stone, "Classification and Regression Trees", New York; Chapman and Hall, 1984.
[5] J. R. Quinlan, "C4.5: Program for Machine Learning", California: Morgan Kaufmann, 1992.
[6] M. R. Segal,"Regression trees for censored data", Biometrics 44, pp.35- 47, 1988.
[7] X.G. Su and J.J. Fan, "Multivariate survival trees: a maximum likelihood approach based on frailty models", Biometrics 60, pp. 93-99, 2004.
[8] F. Gao, A. K. Manatunga, and S. Chen, "Identification of prognostic factors with multivariate survival data", Computational Statistics and Data Analysis 45, pp. 813-824, 2004.
[9] LeBlanc, M., Crowley, J., "Survival trees by goodness of split". Journal of the American Statistical Association 88, 457-467, 1993.
[10] R. Davis and J. Anderson,"Exponential survival trees", Statistics in Medicine 8, pp 947-962, 1989.