The article discusses how combining different types of survival models can improve the accuracy of predicting survival outcomes. Non-parametric models are good when assumptions are uncertain, but parametric and semi-parametric models can be better in small sample sizes. Stacked survival models, which blend different types of models, perform well in various scenarios by balancing strengths and weaknesses. They can predict outcomes as well as or better than models chosen through cross-validation. The researchers applied these models to a breast cancer study and found promising results.