survcox_train

Trains CoxPH using survival package, or trains CoxLasso (cv.glmnet, lambda.min),
and then re-trains survival:coxph on non-zero predictors

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

survcox_train function

<dl><dt>df_train</dt>
<dd>data, "time" and "event" should describe survival outcome</dd>
<dt>predict.factors</dt>
<dd>list of the column names to be used as predictors</dd>
<dt>fixed_time</dt>
<dd>target time, NaN by default; needed here only to re-align with other methods</dd>
<dt>useCoxLasso</dt>
<dd>TRUE or FALSE</dd>
<dt>retrain_cox</dt>
<dd>if useCoxLasso is TRUE, whether to re-train coxph on non-zero predictors, FALSE by default</dd>
<dt>inner_cv</dt>
<dd>k in k-fold CV for training lambda for Cox Lasso, only used for useCoxLasso = TRUE</dd></dl>

Arguments

Trains CoxPH using survival package, or trains CoxLasso (cv.glmnet, lambda.min),
and then re-trains survival:coxph on non-zero predictors — survcox_train

<dl>

<dt>df_train</dt>
<dd>data, "time" and "event" should describe survival outcome</dd>


<dt>predict.factors</dt>
<dd>list of the column names to be used as predictors</dd>


<dt>fixed_time</dt>
<dd>target time, NaN by default; needed here only to re-align with other methods</dd>


<dt>useCoxLasso</dt>
<dd>TRUE or FALSE</dd>


<dt>retrain_cox</dt>
<dd>if useCoxLasso is TRUE, whether to re-train coxph on non-zero predictors, FALSE by default</dd>


<dt>inner_cv</dt>
<dd>k in k-fold CV for training lambda for Cox Lasso, only used for useCoxLasso = TRUE</dd>

</dl>

survcox_train: Trains CoxPH using survival package, or trains CoxLasso (cv.glmnet, lambda.min), and then re-trains survival:coxph on non-zero predictors

Description

Usage

Value

Arguments