combine_cut

A data frame containing information on time-dependent variables
in long format. Needs to contain a "time" variable indicating when the
TDCs change their value. Must have same name as variable indicating event
times in <code>event_df</code>.

tdc_df

A character, specifies the column of the event or
censoring time in <code>event_df</code> and the time of measurement for
the time-dependent covariates in <code>tdc_df</code>.

time_var

As <code>time_var</code>, but specifies column containing the
event indicator. Can be missing in the <code>tdc_df</code>.

status_var

The value that indicates censoring in the
<code>status_var</code> column.

cens_value

Given a data frame with one row per subject containing event times and
a data frame containing time points at which a time-dependent covariate changes
its value, returns the unique time at which either event occurs or a
time-dependent covariate changes its value.

internal

The Piece-wise exponential (Additive Mixed) Model
(PAMM; Bender and Scheipl (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 (2018) <doi: 10.1093/biostatistics/kxy003>.
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.

combine_cut: Extract unique cut points when time-dependent covariates present

Description

Usage

Arguments