Quantile Reparameterized Regression Model and Machine Learning with Long-Term Survivors

We propose a quantile-based survival model that accounts for a cure fraction. By modeling unobserved event causes with a negative binomial distribution and time-to-event with the generalized log-logistic Weibull distribution, we estimate parameters using a frequentist framework. We validated our model and applied it to prostate cancer data to prove its practical use. Comparing it to a Random Survival Forest model, we highlight how different approaches handle long-term survival, ultimately weighing the balance between flexibility, interpretability, and predictive performance.