pammtools provides functions and utilities that facilitate fitting Piece-wise Exponential Additive Mixed Models (PAMMs), including data transformation and other convenience functions for pre- and post-processing as well as plotting.


The best way to get an overview of the functionality provided and how to fit PAMMs is to view the vignettes available at A summary of the vignettes' content is given below:

  • basics: Introduction to PAMMs and basic modeling.

  • baseline: Shows how to estimate and visualize baseline model (without covariates) and comparison to respective Cox-PH model.

  • convenience: Convenience functions for post-processing and plotting PAMMs.

  • data-transformation: Transforming data into a format suitable to fit PAMMs.

  • frailty: Specifying "frailty" terms, i.e., random effects for PAMMs.

  • splines: Specifying spline smooth terms for PAMMs.

  • strata: Specifying stratified models in which each level of a grouping variable has a different baseline hazard.

  • tdcovar: Dealing with time-dependent covariates.

  • tveffects: Specifying time-varying effects.

  • left-truncation: Estimation for left-truncated data.

  • competing-risks: Competing risks analysis.


Bender, Andreas, Andreas Groll, and Fabian Scheipl. 2018. “A Generalized Additive Model Approach to Time-to-Event Analysis” Statistical Modelling, February.

Bender, Andreas, Fabian Scheipl, Wolfgang Hartl, Andrew G. Day, and Helmut Küchenhoff. 2019. “Penalized Estimation of Complex, Non-Linear Exposure-Lag-Response Associations.” Biostatistics 20 (2): 315–31.

Bender, Andreas, and Fabian Scheipl. 2018. “pammtools: Piece-Wise Exponential Additive Mixed Modeling Tools.” ArXiv:1806.01042 Stat, June.