simdf_elra

<p>This is data simulated using the <code>sim_pexp</code> function.
It contains two time-constant and two time-dependent covariates (observed
on different exposure time grids). The code used for simulation is
contained in the examples of <code>?sim_pexp</code>.</p>

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 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

simdf_elra function

<p>An object of class <code>nested_fdf</code> (inherits from <code>sim_df</code>, <code>tbl_df</code>, <code>tbl</code>, <code>data.frame</code>) with 250 rows and 9 columns.</p>

Format

Simulated data with cumulative effects — simdf_elra

Simulated data with cumulative effects

simdf_elra: Simulated data with cumulative effects

Description

Usage

Arguments

Format