  The modeling of time-to-event data, also known as survival analysis, requires
specialized methods that can deal with censoring and truncation, time-varying
features and effects, and that extend to settings with multiple competing
events. However, many machine learning methods for survival analysis only
consider the standard setting with right-censored data and proportional hazards
assumption. The methods that do provide extensions usually address at most a
subset of these challenges and often require specialized software that can not
be integrated into standard machine learning workflows directly. In this work,
we present a very general machine learning framework for time-to-event analysis
that uses a data augmentation strategy to reduce complex survival tasks to
standard Poisson regression tasks. This reformulation is based on well
developed statistical theory. With the proposed approach, any algorithm that
can optimize a Poisson (log-)likelihood, such as gradient boosted trees, deep
neural networks, model-based boosting and many more can be used in the context
of time-to-event analysis. The proposed technique does not require any
assumptions with respect to the distribution of event times or the functional
shapes of feature and interaction effects. Based on the proposed framework we
develop new methods that are competitive with specialized state of the art
approaches in terms of accuracy, and versatility, but with comparatively small
investments of programming effort or requirements for specialized
methodological know-how.
