Reduction techniques for survival analysis

Andreas Bender

Department of Statistics at LMU Munich and

Senior consultant at the Statistical Consulting Unit (StaBLab)


7th December 2023 (Thursday) – 14h:30m


Reduction techniques for survival analysis have become popular in recent years. These transform a survival task into a more standard regression task based on suitable data transformation.
In this talk, we will introduce one such technique, the Piece-wise exponential Additive Mixed Model (PAMM). The talk will illustrate how the model can be used for flexible modeling of covariate effects and accommodate non-proportional hazards. In addition to single-event, right-censored data, the talk will cover left-truncated data, recurrent events, and competing risks.
The talk will provide hands-on examples of how to fit and interpret the model using R Code, based on an implementation in package ‘pammtools’.

Short bio:

Andreas Bender is a postdoctoral researcher and lecturer at the Department of Statistics at LMU Munich at the Chair of Statistical Learning and Data Science and senior consultant at the Statistical Consulting Unit (StaBLab). Obtained his Ph.D. (Dr.rer.nat.) in Statistics. After that he was a Postdoc at the Big Data Institute, University of Oxford, working on spatial analysis in the context of infectious disease mapping, and an Interim Professor at the Institute of Statistics, University of Ulm.
Currently, works as Coordinator of the Machine Learning Consulting Unit and leads the focus group Machine Learning Survival Analysis.
Additionally, he is a Co-founder of the Open Science Initiative in Statistics, Ph.D. Program Coordinator at the Munich Center for Machine Learning, member of mlr-org, and serves a 2-year term as staff representative at the Department of Statistics, LMU Munich.