survival_prob_km

Calculates survival probability estimated by Kaplan-Meier survival curve
Uses polynomial extrapolation in survival function space, using poly(n=3)

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

survival_prob_km function

<dl><dt>df_km_train</dt>
<dd>event probabilities (!not survival)</dd>
<dt>times</dt>
<dd>times at which survival is estimated</dd>
<dt>estimate_censoring</dt>
<dd>FALSE by default, if TRUE, event and censoring is reversed (for IPCW calculations)</dd></dl>

Arguments

Calculates survival probability estimated by Kaplan-Meier survival curve
Uses polynomial extrapolation in survival function space, using poly(n=3) — survival_prob_km

<dl>

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


<dt>times</dt>
<dd>times at which survival is estimated</dd>


<dt>estimate_censoring</dt>
<dd>FALSE by default, if TRUE, event and censoring is reversed (for IPCW calculations)</dd>

</dl>

survival_prob_km: Calculates survival probability estimated by Kaplan-Meier survival curve Uses polynomial extrapolation in survival function space, using poly(n=3)

Description

Usage

Value

Arguments