Application of alternative parametric models for the survival analysis of cancer patients

Abstract

This article describes a methodology that allows an approach to alternative right-censored probabilistic models for the analysis of survival, different to those usually studied (exponential, gamma, Weibull, and log-normal distribution) since it is possible that the data do not always fit with sufficient precision due to existing distributions. The methodology used allows for greater flexibility when modeling extreme observations, generally located in the right tail of data distribution, which admits that some events still have the probability of occurring, which is not the case with traditional models and the Kaplan-Meier estimator, which estimates for the longest times, survival probabilities approximately equal to zero. To show the usefulness of the methodological proposal, we considered an application with real data that relates survival times of patients with colon cancer (CC).