Quantile-based survival prediction reveals how treatment effects vary across the survival distribution—some patients benefit early, others late—information hidden by standard hazard ratios and now achievable without crossing quantile curves.
This paper introduces a method for predicting survival times that captures how different patients respond differently at early vs. late stages of disease. Unlike traditional survival models that give a single risk number, this approach estimates the full distribution of survival times while ensuring predictions stay logically consistent (quantiles don't cross).