  Survival analysis (SA) is an active field of research that is concerned with
time-to-event outcomes and is prevalent in many domains, particularly
biomedical applications. Despite its importance, SA remains challenging due to
small-scale data sets and complex outcome distributions, concealed by
truncation and censoring processes. The piecewise exponential additive mixed
model (PAMM) is a model class addressing many of these challenges, yet PAMMs
are not applicable in high-dimensional feature settings or in the case of
unstructured or multimodal data. We unify existing approaches by proposing
DeepPAMM, a versatile deep learning framework that is well-founded from a
statistical point of view, yet with enough flexibility for modeling complex
hazard structures. We illustrate that DeepPAMM is competitive with other
machine learning approaches with respect to predictive performance while
maintaining interpretability through benchmark experiments and an extended case
study.
