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Flexible Additive Models for Multi-Event Survival Analysis

MCML Authors

Johannes Piller

→ Group Helmut Küchenhoff
Statistical Consulting Unit (StaBLab)

Helmut Küchenhoff

Prof. Dr.

Principal Investigator

Statistical Consulting Unit (StaBLab)

Andreas Bender

Dr.

→ Group Bernd Bischl
Statistical Learning and Data Science

Abstract

Piecewise Exponential Additive Mixed Models (PAMMs) (Bender et al., 2018) have gained popularity in various domains due to their ability to tackle a wide variety of survival problems and their flexibility to model non-linear covariate effects, including time-varying effects and cumulative effects (Bender et al., 2019). One advantage of such reduction techniques is that they do not require any specialised software for the estimation of the model parameters. Thus, in the case of the PAMM, they can be conveniently estimated using generalized additive mixed modeling methodology or, for example, respective boosting or deep learning based approaches (Bender et al., 2022). Nevertheless, their use in practice requires pre-processing, which differs depending on the survival task at hand (e.g. left-truncation, competing risks, etc.) and post-processing (e.g. transforming estimated parameters to useful quantities like survival or transition probabilities). The R package pammtools facilitates the entire modeling process, so far, however, only for single-event data. Here we extend the framework and package capabilities to handle general multi-state models.

inproceedings