International Day Of Women in Statistics and Data Science

Survival analysis plays a fundamental role in health research, especially when the objective is to understand not only the time until an event occurs, but also the possibility that a proportion of individuals may never experience that event. This
situation is particularly relevant in cancer studies, where long-term survivors may be considered cured or no longer susceptible to recurrence or death related to the disease.

In this talk, I will present statistical modeling approaches based on cure models, with emphasis on their application to uterine cancer data. The discussion is motivated by my work on regression modeling for cure factors using the reparametrized defective generalized Gompertz distribution. This framework allows the estimation of both survival behavior and cure proportions, providing a flexible tool for analyzing time-to-event data in the presence of a cured fraction.

The presentation will highlight how cure models can contribute to women’s health research by identifying factors associated with long-term survival and by offering interpretable measures for clinical and epidemiological studies. By connecting statistical theory, survival analysis, and real health data, this work illustrates how statistical science can support evidence-based understanding of cancer outcomes and strengthen global connections between data science and public health.