The Deep Promotion Time Cure Model
Victor Medina-Olivares Stefan Lessmann Nadja Klein
We propose a novel method for predicting time-to-event in the presence of
cure fractions based on flexible survivals models integrated into a deep neural
network framework. Our approach allows for non-linear relationships and
high-dimensional interactions between covariates and survival and is suitable
for large-scale applications. Furthermore, we allow the method to incorporate
an identified predictor formed of an additive decomposition of interpretable
linear and non-linear effects and add an orthogonalization layer to capture
potential higher dimensional interactions. We demonstrate the usefulness and
computational efficiency of our method via simulations and apply it to a large
portfolio of US mortgage loans. Here, we find not only a better predictive
performance of our framework but also a more realistic picture of covariate
effects.