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A Comparative Study of Survival approaches for Breast Cancer Patients

Received: 28 September 2018     Accepted: 11 October 2018     Published: 5 November 2018
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Abstract

A survival analysis model leads one to analyze main factors which impact a patient’s therapy process. In practice a survival analysis is capable of affecting therapeutic protocols. Different methods have been approached to analyze the survival of a breast cancer patient by researchers. The objective of this research is to lead specialists analyzing the breast cancer patients effectively. This research by analyzing 2010 breast cancer patients 1) attempts to propose six different statistical models using parametric and semi-parametric approaches for survival analysis of breast cancer patients, 2) compares the performance capabilities of the proposed statistical models analytically, and 3) addresses the most superior approach for a survival analysis of a breast cancer. To analyze the capability of the six proposed models Akaike term is used. This comprehensive research also indicates that the hazard factors commonly proposed in literature are not capable of leading a specialist to analyze the survival completely. Although it is possible to model the breast cancer survival using different approaches, this research reveals the proposed semi parametric model is capable of providing the most superior condition. The capability of the best parametric model among the five proposed parametric models of this comprehensive research is also addressed. Kaplan-Meier diagram is used to analyze the importance of two new hazard factors proposed in this paper.

Published in Engineering Mathematics (Volume 2, Issue 2)
DOI 10.11648/j.engmath.20180202.11
Page(s) 56-62
Creative Commons

This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.

Copyright

Copyright © The Author(s), 2018. Published by Science Publishing Group

Keywords

Survival Analysis, Breast Cancer, Cox Regression, Semi Parametric Model

References
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[4] Atashgar K, Sheikhaliyan A, Biglarian A. Reviewing and Analyzing Breast Cancer Survival Models. Iranian Journal of Surgery. 2016, 24: 1-16.
[5] Vallinayagam, V, Prathap S, and Venkatesan P. Parametric Regression Models in the Analysis of Breast Cancer Survival Data. International Journal of Science Technology. 2014, 3 (3): 163-167.
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[7] Klein, J. P. and M. L. Moeschberger. Survival analysis: techniques for censored and truncated data. 2th edition. Springer 2005.
[8] Elisa T Lee, Wang JW. Statistical Methods for Survival Data Analysis. 3th edition. John Wiley & Sons Inc; 2003.
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[12] Collett, D. Modeling survival data in medical research.3th edition. New York: Chapman & Hal/CRC; 2003.
[13] Alizadeh A, Mohammadpour R A, Barzegar M A. Comparing Cox Model and Parametric Models in Estimating the Survival Rate of Patients with Prostate Cancer on Radiation Therapy. Journal of Mazandaran University Medicine Science.2013, 23 (100): 21-29.
[14] Nardi, A. and M. Schemper, Comparing Cox and parametric models in clinical studies. Statistics in Medicine.2003, 22 (23): 3597-3610.
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[16] Abadi, A, Yavari P, Dehghani-Arani M, Alavi-Majd H, Ghasemi E, Amanpour F, Bajdik CH. Cox Models Survival Analysis Based on Breast Cancer Treatments. Iranian Journal of Cancer Prevention.2014, 7 (3): 124-129
[17] Fallahzadeh, H, Momayyezi M, Akhundzardeini R, Zarezardeini S. Five year survival of women with breast cancer in Yazd. Asian Pacific Journal of Cancer Prevention.2014, 15: 6597-6601.
[18] Baulies S, Belin L, Mallon P, Senechal C, Pierga J-Y, Cottu P, Sablin M-P, Sastre X, Asselain B, Rouzier R, Reyal F. Time-varying effect and long-term survival analysis in breast cancer patients treated with neoadjuvant chemotherapy. British Journal of Cancer. 2015, 113 (1): 30-36.
[19] Cetin, K, Christiansen C. F, Svaerke C, Jacobsen J. B, Sorensen H. T. Survival in patients with breast cancer with bone metastasis: a Danish population-based cohort study on the prognostic impact of initial stage of disease at breast cancer diagnosis and length of the bone metastasis-free interval. BMJ Open.2015, 5 (4): 1-8.
[20] Rezaianzadeh A, Peacock J, Reidpath D, Talei A, Hosseini S V, Mehrabani D. Survival analysis of 1148 women diagnosed with breast cancer in Southern Iran. BMC Cancer. 2009, 9 (168): 1-11
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Cite This Article
  • APA Style

    Karim Atashgar, Ayeh Sheikhaliyan, Mina Tajvidi, Akbar Biglariyan, Seyed Hadi Molana, et al. (2018). A Comparative Study of Survival approaches for Breast Cancer Patients. Engineering Mathematics, 2(2), 56-62. https://doi.org/10.11648/j.engmath.20180202.11

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    ACS Style

    Karim Atashgar; Ayeh Sheikhaliyan; Mina Tajvidi; Akbar Biglariyan; Seyed Hadi Molana, et al. A Comparative Study of Survival approaches for Breast Cancer Patients. Eng. Math. 2018, 2(2), 56-62. doi: 10.11648/j.engmath.20180202.11

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    AMA Style

    Karim Atashgar, Ayeh Sheikhaliyan, Mina Tajvidi, Akbar Biglariyan, Seyed Hadi Molana, et al. A Comparative Study of Survival approaches for Breast Cancer Patients. Eng Math. 2018;2(2):56-62. doi: 10.11648/j.engmath.20180202.11

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  • @article{10.11648/j.engmath.20180202.11,
      author = {Karim Atashgar and Ayeh Sheikhaliyan and Mina Tajvidi and Akbar Biglariyan and Seyed Hadi Molana and Elnaz Badrkhani Sheikhdarabadi and Masoumeh Tabrizi bahemmat},
      title = {A Comparative Study of Survival approaches for Breast Cancer Patients},
      journal = {Engineering Mathematics},
      volume = {2},
      number = {2},
      pages = {56-62},
      doi = {10.11648/j.engmath.20180202.11},
      url = {https://doi.org/10.11648/j.engmath.20180202.11},
      eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.engmath.20180202.11},
      abstract = {A survival analysis model leads one to analyze main factors which impact a patient’s therapy process. In practice a survival analysis is capable of affecting therapeutic protocols. Different methods have been approached to analyze the survival of a breast cancer patient by researchers. The objective of this research is to lead specialists analyzing the breast cancer patients effectively. This research by analyzing 2010 breast cancer patients 1) attempts to propose six different statistical models using parametric and semi-parametric approaches for survival analysis of breast cancer patients, 2) compares the performance capabilities of the proposed statistical models analytically, and 3) addresses the most superior approach for a survival analysis of a breast cancer. To analyze the capability of the six proposed models Akaike term is used. This comprehensive research also indicates that the hazard factors commonly proposed in literature are not capable of leading a specialist to analyze the survival completely. Although it is possible to model the breast cancer survival using different approaches, this research reveals the proposed semi parametric model is capable of providing the most superior condition. The capability of the best parametric model among the five proposed parametric models of this comprehensive research is also addressed. Kaplan-Meier diagram is used to analyze the importance of two new hazard factors proposed in this paper.},
     year = {2018}
    }
    

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  • TY  - JOUR
    T1  - A Comparative Study of Survival approaches for Breast Cancer Patients
    AU  - Karim Atashgar
    AU  - Ayeh Sheikhaliyan
    AU  - Mina Tajvidi
    AU  - Akbar Biglariyan
    AU  - Seyed Hadi Molana
    AU  - Elnaz Badrkhani Sheikhdarabadi
    AU  - Masoumeh Tabrizi bahemmat
    Y1  - 2018/11/05
    PY  - 2018
    N1  - https://doi.org/10.11648/j.engmath.20180202.11
    DO  - 10.11648/j.engmath.20180202.11
    T2  - Engineering Mathematics
    JF  - Engineering Mathematics
    JO  - Engineering Mathematics
    SP  - 56
    EP  - 62
    PB  - Science Publishing Group
    SN  - 2640-088X
    UR  - https://doi.org/10.11648/j.engmath.20180202.11
    AB  - A survival analysis model leads one to analyze main factors which impact a patient’s therapy process. In practice a survival analysis is capable of affecting therapeutic protocols. Different methods have been approached to analyze the survival of a breast cancer patient by researchers. The objective of this research is to lead specialists analyzing the breast cancer patients effectively. This research by analyzing 2010 breast cancer patients 1) attempts to propose six different statistical models using parametric and semi-parametric approaches for survival analysis of breast cancer patients, 2) compares the performance capabilities of the proposed statistical models analytically, and 3) addresses the most superior approach for a survival analysis of a breast cancer. To analyze the capability of the six proposed models Akaike term is used. This comprehensive research also indicates that the hazard factors commonly proposed in literature are not capable of leading a specialist to analyze the survival completely. Although it is possible to model the breast cancer survival using different approaches, this research reveals the proposed semi parametric model is capable of providing the most superior condition. The capability of the best parametric model among the five proposed parametric models of this comprehensive research is also addressed. Kaplan-Meier diagram is used to analyze the importance of two new hazard factors proposed in this paper.
    VL  - 2
    IS  - 2
    ER  - 

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Author Information
  • Department of Industrial Engineering, Malek Ashtar University of Technology, Tehran, Iran

  • Department of Industrial Engineering, Malek Ashtar University of Technology, Tehran, Iran

  • Radiotherapy and Oncology Specialist, Isfahan University of Medical Science, Isfahan, Iran

  • Department of Biostatistics, Sciences University of Social Welfare & Rehabilitation Sciences, Tehran, Iran

  • Radiotherapy and Oncology Specialist, Iran University of Medical Sciences, Tehran, Iran

  • Department of Industrial Engineering, Malek Ashtar University of Technology, Tehran, Iran

  • Department of Industrial Engineering, Malek Ashtar University of Technology, Tehran, Iran

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