simsurvdata

Function for simulating survival data.

Implementations of classical and machine learning models for survival analysis, including deep neural networks via 'keras' and 'tensorflow'. Each model includes a separated fit and predict interface with consistent prediction types for predicting risk or survival probabilities. Models are either implemented from 'Python' via 'reticulate' <https://CRAN.R-project.org/package=reticulate>, from code in GitHub packages, or novel implementations using 'Rcpp' <https://CRAN.R-project.org/package=Rcpp>. Neural networks are implemented from the 'Python' package 'pycox' <https://github.com/havakv/pycox>.

Yohann Foucher

survivalmodels

Models for Survival Analysis

Raphael Sonabend

simsurvdata function

<dl><dt>n</dt>
<dd><code>(integer(1))</code> Number of samples</dd>
<dt>trt, age, sex</dt>
<dd><code>(numeric(1))</code> Coefficients for covariates.</dd>
<dt>cens</dt>
<dd><code>(numeric(1))</code> Proportion of censoring to be generated, cut-off time is then
selected as the quantile that results in <code>cens</code>.</dd></dl>

Arguments

Simulate Survival Data — simsurvdata

<dl>

<dt>n</dt>
<dd><code>(integer(1))</code> Number of samples</dd>


<dt>trt, age, sex</dt>
<dd><code>(numeric(1))</code> Coefficients for covariates.</dd>


<dt>cens</dt>
<dd><code>(numeric(1))</code> Proportion of censoring to be generated, cut-off time is then
selected as the quantile that results in <code>cens</code>.</dd>

</dl>

simsurvdata: Simulate Survival Data

Description

Usage

Value

Arguments

Details

Examples