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survcompare  

Compares Cox and Survival Random Forests to Quantify Nonlinearity
View on CRAN: Click here


Download and install survcompare package within the R console
Install from CRAN:
install.packages("survcompare")

Install from Github:
library("remotes")
install_github("cran/survcompare")

Install by package version:
library("remotes")
install_version("survcompare", "0.1.2")



Attach the package and use:
library("survcompare")
Maintained by
Diana Shamsutdinova
[Scholar Profile | Author Map]
All associated links for this package
First Published: 2024-01-22
Latest Update: 2024-01-22
Description:
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.
How to cite:
Diana Shamsutdinova (2024). survcompare: Compares Cox and Survival Random Forests to Quantify Nonlinearity. R package version 0.1.2, https://cran.r-project.org/web/packages/survcompare
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