Piece-Wise Exponential Additive Mixed Modeling Tools for Survival Analysis

The Piece-wise exponential (Additive Mixed) Model (PAMM; Bender and others (2018) ) is a powerful model class for the analysis of survival (or time-to-event) data, based on Generalized Additive (Mixed) Models (GA(M)Ms). It offers intuitive specification and robust estimation of complex survival models with stratified baseline hazards, random effects, time-varying effects, time-dependent covariates and cumulative effects (Bender and others (2019)), as well as support for left-truncated data as well as competing risks, recurrent events and multi-state settings. pammtools provides tidy workflow for survival analysis with PAMMs, including data simulation, transformation and other functions for data preprocessing and model post-processing as well as visualization.

pammtools: Piece-Wise Exponential Additive Mixed Modeling Tools

Installation

Install from CRAN or GitHub using:

# CRAN
install.packages("pammtools")
# Development version
remotes::install_github("adibender/pammtools")

Overview

pammtools facilitates the estimation of Piece-wise exponential Additive Mixed Models (PAMMs) for time-to-event data. PAMMs can be represented as generalized additive models and can therefore be estimated using GAM software (e.g. mgcv), which, compared to other packages for survival analysis, often offers more flexibility w.r.t. to the specification of covariate effects (e.g. non-linear, time-varying effects, cumulative effects, etc.). The package supports single-event analysis, left-truncation, recurrent events, competing risks and multi-state models.

To get started, see the Articles section.