survsrf_predict

Predicts event probability for a fitted SRF model randomForestSRC::rfsrc.
Essentially a wrapper of srf_survival_prob_for_time.

Performs repeated nested cross-validation for Cox Proportionate Hazards, Cox Lasso, Survival Random Forest, and their ensemble. Returns internally validated concordance index, time-dependent area under the curve, Brier score, calibration slope, and statistical testing of non-linear ensemble outperforming the baseline Cox model. In this, it helps researchers to quantify the gain of using a more complex survival model, or justify its redundancy. Equally, it shows the performance value of the non-linear and interaction terms, and may highlight the need of further feature transformation. Further details can be found in Shamsutdinova, Stamate, Roberts, & Stahl (2022) "Combining Cox Model and Tree-Based Algorithms to Boost Performance and Preserve Interpretability for Health Outcomes" <doi:10.1007/978-3-031-08337-2_15>, where the method is described as Ensemble 1.

Diana Shamsutdinova

survcompare

Compares Cox and Survival Random Forests to Quantify
Nonlinearity

Daniel Stahl

survsrf_predict function

<dl><dt>trained_model</dt>
<dd>trained model</dd>
<dt>newdata</dt>
<dd>test data</dd>
<dt>fixed_time</dt>
<dd>time for which probabilities are computed</dd>
<dt>oob</dt>
<dd>TRUE/FALSE use out-of-bag predictions while tuning instead of cross-validation, default is TRUE and is faster</dd></dl>

Arguments

Predicts event probability for a fitted SRF model — survsrf_predict

<dl>

<dt>trained_model</dt>
<dd>trained model</dd>


<dt>newdata</dt>
<dd>test data</dd>


<dt>fixed_time</dt>
<dd>time for which probabilities are computed</dd>


<dt>oob</dt>
<dd>TRUE/FALSE use out-of-bag predictions while tuning instead of cross-validation, default is TRUE and is faster</dd>

</dl>

survsrf_predict: Predicts event probability for a fitted SRF model

Description

Usage

Value

Arguments