Training Survival Models using Scoring Rules
Philipp Kopper David Rügamer Raphael Sonabend Bernd Bischl Andreas Bender
Survival Analysis provides critical insights for partially incomplete
time-to-event data in various domains. It is also an important example of
probabilistic machine learning. The probabilistic nature of the predictions can
be exploited by using (proper) scoring rules in the model fitting process
instead of likelihood-based optimization. Our proposal does so in a generic
manner and can be used for a variety of model classes. We establish different
parametric and non-parametric sub-frameworks that allow different degrees of
flexibility. Incorporated into neural networks, it leads to a computationally
efficient and scalable optimization routine, yielding state-of-the-art
predictive performance. Finally, we show that using our framework, we can
recover various parametric models and demonstrate that optimization works
equally well when compared to likelihood-based methods.