loss_one_minus_cd_auc: Calculate Cumulative/Dynamic AUC loss

In survex: Explainable Machine Learning in Survival Analysis

Calculate Cumulative/Dynamic AUC loss

Description

This function subtracts the C/D AUC metric from one to obtain a loss function whose lower values indicate better model performance (useful for permutational feature importance)

Usage

loss_one_minus_cd_auc(y_true = NULL, risk = NULL, surv = NULL, times = NULL)

Arguments

y_true	a survival::Surv object containing the times and statuses of observations for which the metric will be evaluated
risk	ignored, left for compatibility with other metrics
surv	a matrix containing the predicted survival functions for the considered observations, each row represents a single observation, whereas each column one time point
times	a vector of time points at which the survival function was evaluated

Value

a numeric vector of length equal to the length of the times vector, each value (from the range from 0 to 1) represents 1 - AUC metric at a specific time point, with lower values indicating better performance.

#' @section References:

• [1] Uno, Hajime, et al. "Evaluating prediction rules for t-year survivors with censored regression models." Journal of the American Statistical Association 102.478 (2007): 527-537.

• [2] Hung, Hung, and Chin‐Tsang Chiang. "Optimal composite markers for time‐dependent receiver operating characteristic curves with censored survival data." Scandinavian Journal of Statistics 37.4 (2010): 664-679.

See Also

cd_auc()

Examples

library(survival)
library(survex)

cph <- coxph(Surv(time, status) ~ ., data = veteran, model = TRUE, x = TRUE, y = TRUE)
cph_exp <- explain(cph)

y <- cph_exp$y
times <- cph_exp$times
surv <- cph_exp$predict_survival_function(cph, cph_exp$data, times)

loss_one_minus_cd_auc(y, surv = surv, times = times)

survex documentation built on Oct. 25, 2023, 1:06 a.m.