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ISSN: 2155-6180

Journal of Biometrics & Biostatistics
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Research Article

Parametric Cancer Survival Analysis Based on the Boag Model: A Clinician?s View

Shunzo Maetani1* and John W Gamel2

1Tenri Institute of Medical Research, Tenri, Japan

2University of Louisville School of Medicine, Louisville KY, 40202, USA

*Corresponding Author:
Shunzo Maetani
Tenri Institute of Medical Research
200 Mishima-cho, Tenri
632-8552, Japan
Tel: +81-743-63-5611
Fax: +81-743-63-1530
Email: [email protected]

Received date: March 20, 2012; Accepted date: April 25, 2012; Published date: April 26, 2012

Citation: Maetani S, Gamel JW (2012) Parametric Cancer Survival Analysis Based on the Boag Model: A Clinician’s View. J Biomet Biostat S7-017. doi: 10.4172/2155-6180.S7-017

Copyright: © 2012 Maetani S, et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Abstract

As many cancer patients have recently been cured, it has become necessary in cancer survival analysis to distinguish between cure and delayed death, which make a great difference in survival benefit and quality of life. Also, cancer patients must be provided with relevant and comprehensible information to make optimal decisions. For this purpose, the Boag parametric analysis with a cured fraction has emerged as a relevant model. The authors evaluated this model compared with the Cox model using life-long follow-up data. The parameters of the Boag model provided the comprehensible information patients wish to obtain; particularly, the cure rate served as a useful measure of survival benefit. In contrast, the hazard ratio, a parameter of the Cox model, failed to distinguish cure from delayed death. The Boag model could be extended to regression analysis to evaluate the long-term effects of various factors, including cancer treatment. Also, it could be extended to predict the overall survival

curve and mean survival time using limited follow-up data. In conclusion, the Boag model offered a more relevant measure of the long-term benefit of cancer treatment and other factors than conventional methods, although an ideal model has yet to be developed

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