Research Journal of Pharmacy and Technology
SCOPUS
  • Year: 2023
  • Volume: 16
  • Issue: 3

Survival analysis of colon cancer data using quantile regression

  • Author:
  • Bhargavi M Vidya1, Sireesha Veeramachaneni1,*, Venkateswara Rao Mudunuru2
  • Total Page Count: 8
  • Page Number: 1401 to 1408

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 E-mail: vsirisha80@gmail.com

Online Published on 12 October, 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.

Keywords

Colon Cancer, Survival Analysis, Quantile Survival Regression, Kaplan-Meier Analysis, Cox Proportional Hazards Function, Parametric Survival Analysis