survsrf_predict

Predicts event probability by a trained Survival Random Forest

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

Nested Cross-Validation to Compare Cox-PH, Cox-Lasso, Survival
Random Forests

Daniel Stahl

survsrf_predict function

<dl><dt>trained_model</dt>
<dd>a trained SRF model, output of survsrf_train(), or randomForestSRC::rfsrc()</dd>
<dt>newdata</dt>
<dd>new data for which predictions are made</dd>
<dt>fixed_time</dt>
<dd>time of interest for which event probabilities are computed</dd>
<dt>extrapsurvival</dt>
<dd>if probabilities are extrapolated beyond trained times (using probability of
the lastest available time). Can be helpful for cross-validation of small data, where random
split may cause the time of interest being outside of the training set.</dd></dl>

Arguments

Predicts event probability by a trained Survival Random Forest — survsrf_predict

<dl>

<dt>trained_model</dt>
<dd>a trained SRF model, output of survsrf_train(), or randomForestSRC::rfsrc()</dd>


<dt>newdata</dt>
<dd>new data for which predictions are made</dd>


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


<dt>extrapsurvival</dt>
<dd>if probabilities are extrapolated beyond trained times (using probability of
the lastest available time). Can be helpful for cross-validation of small data, where random
split may cause the time of interest being outside of the training set.</dd>

</dl>

survsrf_predict: Predicts event probability by a trained Survival Random Forest

Description

Usage

Value

Arguments