integrated_cd_auc function

Calculate integrated C/D AUC

This function calculates the integrated Cumulative/Dynamic AUC metric for a survival model.

integrated_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

Returns

numeric from 0 to 1, higher values indicate 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.

Details

It is useful to see how a model performs as a whole, not at specific time points, for example for easier comparison. This function allows for calculating the integral of the C/D AUC metric numerically using the trapezoid method.

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)

integrated_cd_auc(y, surv = surv, times = times)

See Also

cd_auc() loss_one_minus_cd_auc()