Dynamic survival metrics evaluate time-dependent survival predictions, producing one metric value per evaluation time point. These metrics assess predicted survival probabilities at specific time points.
Input requirements
truth: asurvival::Surv()object...: list column of data frames containing.eval_time,.pred_survival, and.weight_censoredcolumns
Available metrics
brier_survival()Direction: minimize. Range: [0, 1]
roc_auc_survival()Direction: maximize. Range: [0, 1]
See also
integrated-survival-metrics for integrated survival metrics
static-survival-metrics for static survival metrics
linear-pred-survival-metrics for linear predictor survival metrics
vignette("metric-types") for an overview of all metric types
Examples
library(dplyr)
data("lung_surv")
head(lung_surv)
#> # A tibble: 6 × 4
#> .pred .pred_time surv_obj .pred_linear_pred
#> <list> <dbl> <Surv> <dbl>
#> 1 <tibble [5 × 5]> 324. 306 5.78
#> 2 <tibble [5 × 5]> 476. 455 6.17
#> 3 <tibble [5 × 5]> 521. 1010+ 6.26
#> 4 <tibble [5 × 5]> 368. 210 5.91
#> 5 <tibble [5 × 5]> 506. 883 6.23
#> 6 <tibble [5 × 5]> 324. 1022+ 5.78
lung_surv |>
brier_survival(truth = surv_obj, .pred)
#> # A tibble: 5 × 4
#> .metric .estimator .eval_time .estimate
#> <chr> <chr> <dbl> <dbl>
#> 1 brier_survival standard 100 0.109
#> 2 brier_survival standard 200 0.194
#> 3 brier_survival standard 300 0.219
#> 4 brier_survival standard 400 0.222
#> 5 brier_survival standard 500 0.197