Piece-Wise Exponential Additive Mixed Modeling Tools for Survival Analysis
Add cumulative incidence function to data
Add counterfactual observations for possible transitions
Add predicted (cumulative) hazard to data set
Add survival probability estimates
Add time-dependent covariate to a data set
Embeds the data set with the specified (relative) term contribution
Add transition probabilities confidence intervals
Add transition probabilities
Competing risks trafo
Transform data to Piece-wise Exponential Data (PED)
Transform crps object to data.frame
Calculate confidence intervals
Create a data frame from all combinations of data frames
Calculate difference in cumulative hazards and respective standard err...
Extract cumulative coefficients (cumulative hazard differences)
dplyr Verbs for ped-Objects
Create matrix components for cumulative effects
A formula special used to handle cumulative effect specifications
Extract variables from the left-hand-side of a formula
Extract transition information from different objects
(Cumulative) (Step-) Hazard Plots.
Step ribbon plots.
Calculate CIF for one cause
Calculate (or plot) cumulative effect for all time-points of the follo...
Calculate cumulative hazard
Expand time-dependent covariates to functionals
Obtain interval break points
Exctract event types
Calculate predicted hazard
Information on intervals in which times fall
Construct or extract data that represents a lag-lead window
Extract variables from the left-hand-side of a formula
Extract plot information for all special model terms
Calculate simulation based confidence intervals
helper function for add_trans_ci
Calculate survival probabilities
Extract variables from the left-hand-side of a formula
Extract variables from the left-hand-side of a formula
Extract partial effects for specified model terms
Extract the partial effects of non-linear model terms
Forrest plot of fixed coefficients
Plot Lag-Lead windows
Visualize effect estimates for specific covariate combinations
Plot Normal QQ plots for random effects
Plot 1D (smooth) effects
Plot smooth 1d terms of gam objects
Plot tensor product effects
Checks if data contains timd-dependent covariates
Create start/end times and interval information
Create design matrix from a suitable object
Create design matrix from a suitable object
Calculate the modus
Create nested data frame from data with time-dependent covariates
Construct a data frame suitable for prediction
Fit a piece-wise exponential additive model
pammtools: Piece-wise exponential Additive Mixed Modeling tools.
Extract interval information and median/modus values for covariates
Pipe operator
S3 method for pamm objects for compatibility with package pec
Extract information on concurrent effects
Draw random numbers from piece-wise exponential distribution.
Extract information of the sample contained in a data set
Generate a sequence over the range of a vector
Simulate data for competing risks scenario
Simulate survival times from the piece-wise exponential distribution
New basis for penalized lag selection
Formula specials for defining time-dependent covariates
Split data to obtain recurrent event data in PED format
Function to transform data without time-dependent covariates into piec...
Extract 1d smooth objects in tidy data format.
Extract fixed coefficient table from model object
Extract random effects in tidy data format.
Extract 2d smooth objects in tidy format.
Warn if new t_j are used
Warn if new t_j are used
The Piece-wise exponential (Additive Mixed) Model (PAMM; Bender and others (2018) <doi: 10.1177/1471082X17748083>) 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.