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Statistical Applications of Survival Data Analysis for Breast Cancer Data

Ahammad Basha Shaik*, Venkataramanaiah. M**, Thasleema***

*-*** Research Scholar, Department of Statistics, Sri Venkateswara University, Tirupati, Andhra Pradesh, India.

** Professor, Department of Statistics, Sri Venkateswara University, Tirupati, Andhra Pradesh, India.

Periodicity:April - June'2015
DOI : https://doi.org/10.26634/jmat.4.2.3499

Abstract

Breast cancer is the most common of all cancers and is the leading cause of cancer deaths among women worldwide. Cancer is the second leading cause of death in worldwide, with more than 12 million new cases every year. Patients with advanced cancer survival of the stomach, bronchus, colon, ovary, or breast were treated. Cancer related mortality has been estimated to about 7.6 million in 2007. Among them, Breast cancer is the most common cancer in women all over India and accounts for 25% to 31% of all cancers in women in Indian cities. In this paper, survival pattern of cancer patients was studied and survival estimates were calculated using the Kaplan-Meier method. Log rank test was used to test the equality of the groups over the survival distribution estimates. The results of the Cox regression analysis show that the hazard ratio for death due to breast cancer in women with an age group (≤30 Years Vs ≥ 50 Years: hazard ratio = 3.704, 95% CI; 2.172–6.316), stage (Early Vs Advanced: hazard ratio = 9.635,95% CI; 4.268–21.75), grade (Poor Vs High: hazard ratio = 0.968, 95% CI; 0.292-3.209) and tumour size (≤ 2 cm Vs ≥ 5cm: hazard ratio = 13.156, 95% CI; 3.018- 57.342) were significantly related to survival. Breast cancer is generally detected at advanced stages when a cure is not possible. The incidence of breast cancer increases with increasing age across the globe.

Keywords

Breast Cancer; Kaplan-Meier Estimate; Log-Rank Test; Cox Regression

How to Cite this Article?

Shaik, A.B., Venkataramanaiah, M., and Thasleema, S,C. (2015). Statistical Applications of Survival Data Analysis for Breast Cancer Data. i-manager’s Journal on Mathematics, 4(2), 30-36. https://doi.org/10.26634/jmat.4.2.3499

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