integrated_cd_auc: Calculate integrated C/D AUC
| integrated_cd_auc | R Documentation |
Calculate integrated C/D AUC
Description
This function calculates the integrated Cumulative/Dynamic AUC metric for a survival model.
Usage
integrated_cd_auc(y_true = NULL, risk = NULL, surv = NULL, times = NULL)
Arguments
y_true |
a |
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 |
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.
Value
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.
See Also
cd_auc() loss_one_minus_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)
integrated_cd_auc(y, surv = surv, times = times)
