get_surv_prob

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_surv_prob function

<dl><dt>newdata</dt>
<dd>A data frame or list containing the values of the model covariates at which predictions
 are required. If this is not provided then predictions corresponding to the
 original data are returned. If <code>newdata</code> is provided then
 it should contain all the variables needed for prediction: a
 warning is generated if not. See details for use with <code>link{linear.functional.terms}</code>.</dd>
<dt>object</dt>
<dd>a fitted <code>gam</code> object as produced by <code>gam()</code>.</dd>
<dt>ci</dt>
<dd><code>logical</code>. Indicates if confidence intervals should be
calculated. Defaults to <code>TRUE</code>.</dd>
<dt>se_mult</dt>
<dd>Factor by which standard errors are multiplied for calculating
the confidence intervals.</dd>
<dt>time_var</dt>
<dd>Name of the variable used for the baseline hazard. If
not given, defaults to <code>"tend"</code> for <code><a href="/link/gam?package=pammtools&version=0.7.3" data-mini-rdoc="pammtools::gam">gam</a></code> fits, else
<code>"interval"</code>. The latter is assumed to be a factor, the former
numeric.</dd>
<dt>interval_length</dt>
<dd>The variable in newdata containing the interval lengths.
Can be either bare unquoted variable name or character. Defaults to <code>"intlen"</code>.</dd>
<dt>...</dt>
<dd>Further arguments passed to <code><a href="/link/predict.gam?package=pammtools&version=0.7.3" data-mini-rdoc="pammtools::predict.gam">predict.gam</a></code> and
<code>get_hazard</code></dd></dl>

Arguments

Calculate survival probabilities — get_surv_prob

<dl>

<dt>newdata</dt>
<dd>A data frame or list containing the values of the model covariates at which predictions
 are required. If this is not provided then predictions corresponding to the
 original data are returned. If <code>newdata</code> is provided then
 it should contain all the variables needed for prediction: a
 warning is generated if not. See details for use with <code>link{linear.functional.terms}</code>.</dd>


<dt>object</dt>
<dd>a fitted <code>gam</code> object as produced by <code>gam()</code>.</dd>


<dt>ci</dt>
<dd><code>logical</code>. Indicates if confidence intervals should be
calculated. Defaults to <code>TRUE</code>.</dd>


<dt>se_mult</dt>
<dd>Factor by which standard errors are multiplied for calculating
the confidence intervals.</dd>


<dt>time_var</dt>
<dd>Name of the variable used for the baseline hazard. If
not given, defaults to <code>"tend"</code> for <code><a href='https://rdrr.io/pkg/mgcv/man/gam.html'>gam</a></code> fits, else
<code>"interval"</code>. The latter is assumed to be a factor, the former
numeric.</dd>


<dt>interval_length</dt>
<dd>The variable in newdata containing the interval lengths.
Can be either bare unquoted variable name or character. Defaults to <code>"intlen"</code>.</dd>


<dt>...</dt>
<dd>Further arguments passed to <code><a href='https://rdrr.io/pkg/mgcv/man/predict.gam.html'>predict.gam</a></code> and
<code>get_hazard</code></dd>

</dl>

Calculate survival probabilities

get_surv_prob: Calculate survival probabilities

Description

Usage

Arguments