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Survival analysis (SA) is an active field of research that is concerned withtime-to-event outcomes and is prevalent in many domains, particularlybiomedical applications. Despite its importance, SA remains challenging due tosmall-scale data sets and complex outcome distributions, concealed bytruncation and censoring processes. The piecewise exponential additive mixedmodel (PAMM) is a model class addressing many of these challenges, yet PAMMsare not applicable in high-dimensional feature settings or in the case ofunstructured or multimodal data. We unify existing approaches by proposingDeepPAMM, a versatile deep learning framework that is well-founded from astatistical point of view, yet with enough flexibility for modeling complexhazard structures. We illustrate that DeepPAMM is competitive with othermachine learning approaches with respect to predictive performance whilemaintaining interpretability through benchmark experiments and an extended casestudy.

DeepPAMM: Deep Piecewise Exponential Additive Mixed Models for Complex Hazard Structures in Survival Analysis