This function calculates the Cumulative/Dynamic AUC metric for a survival model. It is done using the estimator proposed proposed by Uno et al. [1], and Hung and Chang [2].
cd_auc(y_true = NULL, risk = NULL, surv = NULL, times = NULL)a survival::Surv object containing the times and statuses of observations for which the metric will be evaluated
ignored, left for compatibility with other metrics
a matrix containing the predicted survival functions for the considered observations, each row represents a single observation, whereas each column one time point
a vector of time points at which the survival function was evaluated
a numeric vector of length equal to the length of the times vector, each value (from the range from 0 to 1) represents the AUC metric at a specific time point, with higher values indicating better performance.
C/D AUC is an extension of the AUC metric known from classification models. Its values represent the model's performance at specific time points. It can be integrated over the considered time range.
[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.
library(survival)
library(survex)
cph <- coxph(Surv(time, status) ~ ., data = veteran, model = TRUE, x = TRUE, y = TRUE)
cph_exp <- explain(cph)
#> Preparation of a new explainer is initiated
#> -> model label : coxph ( default )
#> -> data : 137 rows 6 cols ( extracted from the model )
#> -> target variable : 137 values ( 128 events and 9 censored , censoring rate = 0.066 ) ( extracted from the model )
#> -> times : 50 unique time points , min = 1.5 , median survival time = 80 , max = 999
#> -> times : ( generated from y as uniformly distributed survival quantiles based on Kaplan-Meier estimator )
#> -> predict function : predict.coxph with type = 'risk' will be used ( default )
#> -> predict survival function : predictSurvProb.coxph will be used ( default )
#> -> predict cumulative hazard function : -log(predict_survival_function) will be used ( default )
#> -> model_info : package survival , ver. 3.7.0 , task survival ( default )
#> A new explainer has been created!
y <- cph_exp$y
times <- cph_exp$times
surv <- cph_exp$predict_survival_function(cph, cph_exp$data, times)
cd_auc(y, surv = surv, times = times)
#> [1] 0.6592593 0.8045455 0.8352713 0.7846667 0.7845528 0.7411765 0.7506690
#> [8] 0.7628458 0.7982143 0.8151515 0.8487523 0.8512279 0.8333333 0.8296703
#> [15] 0.8165414 0.8084541 0.8184397 0.7926437 0.8245732 0.8392857 0.8405041
#> [22] 0.8230108 0.8264127 0.8191898 0.8432836 0.8375428 0.8454545 0.8485378
#> [29] 0.8353549 0.8234483 0.8033097 0.7910557 0.7764411 0.7830979 0.7830033
#> [36] 0.7801256 0.7964090 0.7876756 0.7825000 0.7643021 0.7701149 0.7852941
#> [43] 0.7775956 0.7816377 0.7849928 0.8149225 0.8689567 0.8120301 0.8740741
#> [50] NaN