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 https://adibender.github.io/pammtools/articles/. 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. https://doi.org/10.1177/1471082X17748083.
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. https://doi.org/10.1093/biostatistics/kxy003.
Bender, Andreas, and Fabian Scheipl. 2018. “pammtools: Piece-Wise Exponential Additive Mixed Modeling Tools.” ArXiv:1806.01042 Stat, June. https://arxiv.org/abs/1806.01042.