surv_brierscore

Calculates time-dependent Brier Scores for a vector of times. Calculations are similar to that in:
https://scikit-survival.readthedocs.io/en/stable/api/generated/sksurv.metrics.brier_score.html#sksurv.metrics.brier_score
https://github.com/sebp/scikit-survival/blob/v0.19.0.post1/sksurv/metrics.py#L524-L644
The function uses IPCW (inverse probability of censoring weights), computed using the Kaplan-Meier
survival function, where events are censored events from train data

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

surv_brierscore function

<dl><dt>y_predicted_newdata</dt>
<dd>computed event probabilities (! not survival probabilities)</dd>
<dt>df_brier_train</dt>
<dd>train data</dd>
<dt>df_newdata</dt>
<dd>test data for which brier score is computed</dd>
<dt>time_point</dt>
<dd>times at which BS calculated</dd>
<dt>weighted</dt>
<dd>TRUE/FALSE for IPWC to use or not</dd></dl>

Arguments

Calculates time-dependent Brier Score — surv_brierscore

<dl>

<dt>y_predicted_newdata</dt>
<dd>computed event probabilities (! not survival probabilities)</dd>


<dt>df_brier_train</dt>
<dd>train data</dd>


<dt>df_newdata</dt>
<dd>test data for which brier score is computed</dd>


<dt>time_point</dt>
<dd>times at which BS calculated</dd>


<dt>weighted</dt>
<dd>TRUE/FALSE for IPWC to use or not</dd>

</dl>

surv_brierscore: Calculates time-dependent Brier Score

Description

Usage

Value

Arguments