has_tdc

Checks if data contains timd-dependent covariates

internal

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 data as well as competing risks,
recurrent events and multi-state settings.
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

Johannes Piller

Philipp Kopper

Lukas Burk

has_tdc function

<dl><dt>data</dt>
<dd>A data frame (potentially) containing time-dependent covariates.</dd>
<dt>id_var</dt>
<dd>A character indicating the grouping variable. For each covariate
it will be checked if their values change within a group specified by
<code>id_var</code>.</dd></dl>

Arguments

Checks if data contains timd-dependent covariates — has_tdc

<dl>

<dt>data</dt>
<dd>A data frame (potentially) containing time-dependent covariates.</dd>


<dt>id_var</dt>
<dd>A character indicating the grouping variable. For each covariate
it will be checked if their values change within a group specified by
<code>id_var</code>.</dd>

</dl>

has_tdc: Checks if data contains timd-dependent covariates

Description

Usage

Value

Arguments