get_cif

get_cif.default

Internal generic dispatching CIF calculation based on the model class.

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

get_cif function

<dl><dt>newdata</dt>
<dd>A data frame of new observations, typically created via
<code>make_newdata()</code>.</dd>
<dt>object</dt>
<dd>A fitted model object. The method is dispatched on this
argument.</dd>
<dt>...</dt>
<dd>Additional arguments passed to the respective method.</dd></dl>

Arguments

Calculate CIF for one cause — get_cif

<dl>

<dt>newdata</dt>
<dd>A data frame of new observations, typically created via
<code>make_newdata()</code>.</dd>


<dt>object</dt>
<dd>A fitted model object. The method is dispatched on this
argument.</dd>


<dt>...</dt>
<dd>Additional arguments passed to the respective method.</dd>

</dl>

get_cif: Calculate CIF for one cause

Description

Usage

Value

Arguments