Flexible Additive Models for Survival and Event-History Analysis - Andreas Bender & Johannes Piller

The Piecewise Exponential Additive Mixed Model (PAMM) has gained popularity in various domains due to its ability to tackle a wide variety of survival tasks and its flexibility to model multivariate non-linear covariate effects, including time-varying effects and cumulative effects. One advantage of this model class is the ability to use different backends for estimation. However, in order to be useful in practice, their use requires pre-processing, which differs depending on the survival task at hand and post-processing (e.g. transforming estimated parameters to quantities like survival or transition probabilities). The R package pammtools (adibender.gith...) facilitates the entire modeling process. In this tutorial, we illustrate how to apply the model class in different settings, including left-truncation, recurrent events and multi-state models.
Johannes Piller, LMU
Johannes Piller is a doctoral candidate at the department of statistics at LMU Munich, specializing in statistical modeling. Prior, he completed his master’s degree in Mathematics at TU Munich.
Andreas Bender, LMU
Andreas Bender is a postdoctoral lecturer and researcher at the department of statistics of LMU Munich, with interest in (machine learning) survival analysis.