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 represeted 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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Install

install.packages('pammtools')

Monthly Downloads

5,663

Version

0.1.14

License

MIT + file LICENSE

Issues

Pull Requests

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Repository

https://github.com/adibender/pammtools

Maintainer

Andreas Bender

Last Published

September 8th, 2019

Functions in pammtools (0.1.14)

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

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

Transform data to Piece-wise Exponential Data (PED)

Add time-dependent covariate to a data set

Add predicted (cumulative) hazard to data set

Extract cumulative coefficients (cumulative hazard differences)

Extract unique cut points when time-dependent covariates present

Add survival probability estimates

A formula special used to handle cumulative effect specifications

Plot 1D (smooth) effects

Plot tensor product effects

geom_stepribbon

Step ribbon plots.

Expand time-dependent covariates to functionals

Plot smooth 1d terms of gam objects

(Cumulative) (Step-) Hazard Plots.

get_cumu_hazard

Calculate cumulative hazard

Plot Normal QQ plots for random effects

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

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

get_cumu_coef_baseline

Extract cumulative coefficients (cumulative hazard differences)

Calculate predicted hazard

Time until extubation

Create matrix components for cumulative effects

Obtain interval break points

Construct a data frame suitable for prediction

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

Calculate survival probabilities

Extract time-dependent covariates from data set

Calculate simulation based confidence intervals

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

Extract plot information for all special model terms

Extract 1d smooth objects in tidy data format.

pammtools: Piece-wise exponential Additive Mixed Modeling tools.

Plot Lag-Lead windows

Visualize effect estimates for specific covariate combinations

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

Time-dependent covariates of the patient data set.

dplyr Verbs for ped-Objects

Extract partial effects for specified model terms

Extract interval information and median/modus values for covariates

Survival data of critically ill ICU patients

Simulated data with cumulative effects

Information on intervals in which times fall

Extract risk set information for each interval.

Construct or extract data that represents a lag-lead window

Extract information of the sample contained in a data set

Forrest plot of fixed coefficients

Create start/end times and interval information

Extract time-dependent covariates from data set

Extract the partial effects of non-linear model terms

prep_concurrent

Extract information on concurrent effects

Extract 2d smooth objects in tidy format.

tidyr Verbs for ped-Objects

Formula specials for defining time-dependent covariates

Extract fixed coefficient table from model object

Extract random effects in tidy data format.

warn_about_new_time_points

Warn if new t_j are used

Stomach area tumor data

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

Calculate the modus

Create nested data frame from data with time-dependent covariates

Generate a sequence over the range of a vector

Simulate survival times from the piece-wise exponential distribution