survcompare2: Compares two cross-validated models using surv____cv...

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

Compares two cross-validated models using surv____cv functions of this package.

Description

#' The two arguments are two cross-validated models, base and alternative, e.g., Cox Proportionate Hazards Model (or Cox LASSO), and Survival Random Forest, or DeepHit (if installed from GitHub, not in CRAN version). Please see examples below.

Both cross-validations should be done with the same random seed, number of repetitions (repeat_cv), outer_cv and inner_cv to ensure the models are compared on the same train/test splits.

Harrel's c-index,time-dependent AUC-ROC, time-dependent Brier Score, and calibration slopes are reported. The statistical significance of the performance differences is tested for the C-indeces.

The function is designed to help with the model selection by quantifying the loss of predictive performance (if any) if "alternative" is used instead of "base."

Usage

survcompare2(base, alternative)

Arguments

base	an object of type "survensemble_cv", for example, outcomes of survcox_cv, survsrf_cv, survsrfens_cv, survsrfstack_cv
alternative	an object of type "survensemble_cv", to compare to "base"

Value

outcome = list(data frame with performance results, fitted Cox models, fitted DeespSurv)

Examples

df <-simulate_nonlinear(100)
params <- names(df)[1:4]
cv1 <- survcox_cv(df, params, randomseed = 42, repeat_cv =1)
cv2 <- survsrf_cv(df, params, randomseed = 42, repeat_cv = 1)
survcompare2(cv1, cv2)

survcompare documentation built on June 25, 2025, 5:09 p.m.