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`pammtools`: Piece-Wise Exponential Additive Mixed Modeling Tools

Installation

Install from CRAN or GitHub using:

# CRAN
install.packages("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.).

To get started, see the Articles section.

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Version

Install

install.packages('pammtools')

Monthly Downloads

3,693

Version

0.5.91

License

MIT + file LICENSE

Maintainer

Andreas Bender

Last Published

March 24th, 2023

Functions in pammtools (0.5.91)

compute_cumu_diff

Calculate difference in cumulative hazards and respective standard errors

Extract cumulative coefficients (cumulative hazard differences)

geom_stepribbon

Step ribbon plots.

Create matrix components for cumulative effects

Time-dependent covariates of the patient data set.

A formula special used to handle cumulative effect specifications

dplyr Verbs for ped-Objects

get_event_types

Exctract event types

Extract plot information for all special model terms

Extract variables from the left-hand-side of a formula

Calculate CIF for one cause

Calculate predicted hazard

Construct or extract data that represents a lag-lead window

Information on intervals in which times fall

Expand time-dependent covariates to functionals

Obtain interval break points

Visualize effect estimates for specific covariate combinations

Plot Normal QQ plots for random effects

Calculate (or plot) cumulative effect for all time-points of the follow-up

Extract variables from the left-hand-side of a formula

(Cumulative) (Step-) Hazard Plots.

Forrest plot of fixed coefficients

Calculate simulation based confidence intervals

Calculate survival probabilities

get_cumu_hazard

Calculate cumulative hazard

Plot Lag-Lead windows

Extract variables from the left-hand-side of a formula

Extract variables from the left-hand-side of a formula

Plot 1D (smooth) effects

pammtools: Piece-wise exponential Additive Mixed Modeling tools.

prep_concurrent

Extract information on concurrent effects

Fit a piece-wise exponential additive model

Extract the partial effects of non-linear model terms

Plot tensor product effects

Plot smooth 1d terms of gam objects

Extract partial effects for specified model terms

warn_about_new_time_points

Warn if new t_j are used

Extract interval information and median/modus values for covariates

Construct a data frame suitable for prediction

Checks if data contains timd-dependent covariates

Create nested data frame from data with time-dependent covariates

Stomach area tumor data

Survival data of critically ill ICU patients

Simulate survival times from the piece-wise exponential distribution

Function to transform data without time-dependent covariates into piece-wise exponential data format

Draw random numbers from piece-wise exponential distribution.

Formula specials for defining time-dependent covariates

Extract 1d smooth objects in tidy data format.

predictSurvProb.pamm

S3 method for pamm objects for compatibility with package pec

Extract fixed coefficient table from model object

Simulated data with cumulative effects

Extract random effects in tidy data format.

Extract 2d smooth objects in tidy format.

Calculate the modus

Create start/end times and interval information

Extract information of the sample contained in a data set

Generate a sequence over the range of a vector

Time until staphylococcus aureaus infection in children, with possible recurrence

split_data_recurrent

Split data to obtain recurrent event data in PED format

Add survival probability estimates

Transform data to Piece-wise Exponential Data (PED)

Add cumulative incidence function to data

Embeds the data set with the specified (relative) term contribution

Add time-dependent covariate to a data set

as.data.frame.crps

Transform crps object to data.frame

Add predicted (cumulative) hazard to data set

Create a data frame from all combinations of data frames

Calculate confidence intervals

Competing risks trafo