extub_event

extub_tdc

This is a preprocessed
subset of the data discussed in Heyard, et. al 2018 (and provided in a slightly
different format as <code>VAP_data</code> in the package <code>TBFmultinomial</code>).
In this package, the data is split in two parts, <code>extub_event</code> contains
time-to-event data and time-constant covariates and <code>extub_tdc</code> contains
the information on the time-dependent covariate SOFA score.
The data contains the following variables:<dl class="dl-horizontal">
<dt>ID</dt><dd>Unique patient ID</dd>
<dt>gender</dt><dd>The patients' gender</dd>
<dt>type</dt><dd>Type of admission, either <code>Medical</code> or <code>Surgical</code></dd>
<dt>SAPSadmission</dt><dd>SAPS score at admission</dd>
<dt>time</dt><dd>Time (days) until extubation</dd>
<dt>extubation</dt><dd>0 = no extubation/censoring, 1 = extubation</dd>
<dt>day</dt><dd>Exposure time, i.e., time at which the SOFA score was observed</dd>
<dt>SOFA</dt><dd>The SOFA score at respective <code>day</code>s</dd>
</dl>

datasets

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, competing risks and recurrent events data.
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.

Andreas Bender

pammtools

Piece-Wise Exponential Additive Mixed Modeling Tools for
Survival Analysis

Fabian Scheipl

Philipp Kopper

extub_event function

An object of class <code>tbl_df</code> (inherits from <code>tbl</code>, <code>data.frame</code>) with 110 rows and 6 columns.
An object of class <code>tbl_df</code> (inherits from <code>tbl</code>, <code>data.frame</code>) with 1518 rows and 3 columns.

Format

This is a preprocessed
subset of the data discussed in Heyard, et. al 2018 (and provided in a slightly
different format as <code>VAP_data</code> in the package <code>TBFmultinomial</code>).
In this package, the data is split in two parts, <code>extub_event</code> contains
time-to-event data and time-constant covariates and <code>extub_tdc</code> contains
the information on the time-dependent covariate SOFA score.
The data contains the following variables:<dl class='dl-horizontal'>
<dt>ID</dt><dd>Unique patient ID</dd>
<dt>gender</dt><dd>The patients' gender</dd>
<dt>type</dt><dd>Type of admission, either <code>Medical</code> or <code>Surgical</code></dd>
<dt>SAPSadmission</dt><dd>SAPS score at admission</dd>
<dt>time</dt><dd>Time (days) until extubation</dd>
<dt>extubation</dt><dd>0 = no extubation/censoring, 1 = extubation</dd>
<dt>day</dt><dd>Exposure time, i.e., time at which the SOFA score was observed</dd>
<dt>SOFA</dt><dd>The SOFA score at respective <code>day</code>s</dd>
</dl>

extub_event: Time until extubation

Description

Usage

Arguments

Format