Author(s):
Vidya Bhargavi M, Sireesha Veeramachaneni, Venkateswara Rao Mudunuru
Email(s):
vsirisha80@gmail.com
DOI:
10.52711/0974-360X.2023.00231 
Address:
Vidya Bhargavi M1, Sireesha Veeramachaneni1*, Venkateswara Rao Mudunuru2
1GITAM Institute of Science, GITAM (deemed to be) University, Visakhapatnam, Andhra Pradesh, India.
2Department of Mathematics and Statistics, University of South Florida, Tampa, FL, USA.
*Corresponding Author
Published In:
Volume - 16,
Issue - 3,
Year - 2023
ABSTRACT:
Quantile regression emerged as an alternative and robust technique to the commonly used regression models. Even in the survival analysis, quantile regression is offering more flexible modelling of survival data without any constraints attached. Unlike traditional Cox hazards models or accelerated failure models, quantile regression does not restrict the variation of the coefficients for different quantiles. In this research we modelled and compared traditional survival regression method with quantile regression applied to colon cancer data.
Cite this article:
Vidya Bhargavi M, Sireesha Veeramachaneni, Venkateswara Rao Mudunuru. Survival Analysis of Colon Cancer Data using Quantile Regression. Research Journal of Pharmacy and Technology 2023; 16(3):1401-8. doi: 10.52711/0974-360X.2023.00231
Cite(Electronic):
Vidya Bhargavi M, Sireesha Veeramachaneni, Venkateswara Rao Mudunuru. Survival Analysis of Colon Cancer Data using Quantile Regression. Research Journal of Pharmacy and Technology 2023; 16(3):1401-8. doi: 10.52711/0974-360X.2023.00231 Available on: https://www.rjptonline.org/AbstractView.aspx?PID=2023-16-3-67
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