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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Install

install.packages('pammtools')

Monthly Downloads

3,503

Version

0.2.3

License

MIT + file LICENSE

Issues

Pull Requests

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Repository

https://github.com/adibender/pammtools

Maintainer

Andreas Bender

Last Published

May 27th, 2020

Functions in pammtools (0.2.3)

Add predicted (cumulative) hazard to data set

as.data.frame.crps

Transform crps object to data.frame

Create a data frame from all combinations of data frames

Transform data to Piece-wise Exponential Data (PED)

Add time-dependent covariate to a data set

Add survival probability estimates

Extract unique cut points when time-dependent covariates present

Calculate confidence intervals

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

compute_cumu_diff

Calculate difference in cumulative hazards and respective standard errors

geom_stepribbon

Step ribbon plots.

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

Information on intervals in which times fall

Construct or extract data that represents a lag-lead window

Time until extubation

Extract the partial effects of non-linear model terms

Plot Normal QQ plots for random effects

Plot 1D (smooth) effects

pammtools: Piece-wise exponential Additive Mixed Modeling tools.

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

(Cumulative) (Step-) Hazard Plots.

A formula special used to handle cumulative effect specifications

Extract plot information for all special model terms

dplyr Verbs for ped-Objects

Calculate simulation based confidence intervals

Forrest plot of fixed coefficients

Visualize effect estimates for specific covariate combinations

Create matrix components for cumulative effects

Plot Lag-Lead windows

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

Calculate survival probabilities

Survival data of critically ill ICU patients

Extract cumulative coefficients (cumulative hazard differences)

Extract time-dependent covariates from data set

Obtain interval break points

Calculate the modus

Extract 1d smooth objects in tidy data format.

Construct a data frame suitable for prediction

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

Plot smooth 1d terms of gam objects

Time-dependent covariates of the patient data set.

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

Extract 2d smooth objects in tidy format.

Extract interval information and median/modus values for covariates

get_cumu_hazard

Calculate cumulative hazard

Create nested data frame from data with time-dependent covariates

Expand time-dependent covariates to functionals

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

Calculate predicted hazard

Extract time-dependent covariates from data set

predictSurvProb.pamm

S3 method for pamm objects for compatibility with package pec

prep_concurrent

Extract information on concurrent effects

Fit a piece-wise exponential additive model

Extract partial effects for specified model terms

tidyr Verbs for ped-Objects

Plot tensor product effects

Simulated data with cumulative effects

Draw random numbers from piece-wise exponential distribution.

Generate a sequence over the range of a vector

Create start/end times and interval information

Extract information of the sample contained in a data set

Stomach area tumor data

Extract random effects in tidy data format.

Simulate survival times from the piece-wise exponential distribution

warn_about_new_time_points

Warn if new t_j are used

Formula specials for defining time-dependent covariates

Extract fixed coefficient table from model object