Exponentiated Weibull Mixture Cure Model to Handle Right-Censored Data Set

Survival analysis is an important statistical tool for analyzing time-to-event data, such as the time to failure of a product or the time to death in medical research. Often, these data sets are subject to right-censoring, where some individuals are still alive or have not experienced the event of interest at the end of the study period. However, the cure rate models are frequently used to model this type of data. The models provide estimates of the fraction of patients cured of disease as well as the distribution of survival times for uncured patients. In this paper, we propose an exponentiated Weibull mixture cure model, to handle right-censored data sets in survival analysis. The model assumes that the population is a mixture of two sub-populations: a cured population that will never experience the event of interest, and a susceptible population that will experience the event of interest at some point in time. The proposed model applied to the real-life data set and compared its performance with other commonly used survival models. The results show that the exponentiated Weibull mixture cure model provides a better fit to the data and more accurate predictions of survival than other models.