pammtools package - RDocumentation

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

Installation

Install from CRAN or GitHub using:

# CRAN
install.packages("pammtools")

# GitHub
devtools::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.).

To get started, see the Articles section.

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Version

Install

install.packages('pammtools')

Monthly Downloads

3,693

Version

0.5.7

License

MIT + file LICENSE

Maintainer

Andreas Bender

Last Published

June 21st, 2021

Functions in pammtools (0.5.7)

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

Add survival probability estimates

Transform data to Piece-wise Exponential Data (PED)

Add time-dependent covariate to a data set

as.data.frame.crps

Transform crps object to data.frame

Add cumulative incidence function to data

Competing risks trafo

Calculate confidence intervals

Create a data frame from all combinations of data frames

compute_cumu_diff

Calculate difference in cumulative hazards and respective standard errors

Calculate predicted hazard

Time-dependent covariates of the patient data set.

dplyr Verbs for ped-Objects

Add predicted (cumulative) hazard to data set

A formula special used to handle cumulative effect specifications

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

Extract the partial effects of non-linear model terms

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

Calculate CIF for one cause

get_cumu_hazard

Calculate cumulative hazard

Expand time-dependent covariates to functionals

Information on intervals in which times fall

Create matrix components for cumulative effects

Forrest plot of fixed coefficients

Time until extubation

Extract cumulative coefficients (cumulative hazard differences)

(Cumulative) (Step-) Hazard Plots.

Plot 1D (smooth) effects

Plot Normal QQ plots for random effects

Extract interval information and median/modus values for covariates

Extract 2d smooth objects in tidy format.

Visualize effect estimates for specific covariate combinations

Plot Lag-Lead windows

Construct a data frame suitable for prediction

Fit a piece-wise exponential additive model

tidyr Verbs for ped-Objects

Extract plot information for all special model terms

Calculate simulation based confidence intervals

Checks if data contains timd-dependent covariates

pammtools: Piece-wise exponential Additive Mixed Modeling tools.

Construct or extract data that represents a lag-lead window

geom_stepribbon

Step ribbon plots.

Survival data of critically ill ICU patients

Extract fixed coefficient table from model object

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

predictSurvProb.pamm

S3 method for pamm objects for compatibility with package pec

Plot tensor product effects

Plot smooth 1d terms of gam objects

Create start/end times and interval information

Obtain interval break points

prep_concurrent

Extract information on concurrent effects

Time until staphylococcus aureaus infection in children, with possible recurrence

Calculate survival probabilities

Extract random effects in tidy data format.

Stomach area tumor data

Extract 1d smooth objects in tidy data format.

warn_about_new_time_points

Warn if new t_j are used

Extract information of the sample contained in a data set

Draw random numbers from piece-wise exponential distribution.

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

Formula specials for defining time-dependent covariates

Simulated data with cumulative effects

get_event_types

Exctract event types

Calculate the modus

Extract partial effects for specified model terms

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

Generate a sequence over the range of a vector

Create nested data frame from data with time-dependent covariates

Simulate survival times from the piece-wise exponential distribution

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

split_data_recurrent

Split data to obtain recurrent event data in PED format