AUC confidence interval about cutpointr

[Thie1e]

Hi, yes, you can. We tend to favor the use of nonparametric methods, so you can calculate confidence intervals for the AUC (and all other metrics) using the bootstrap routine. You get basic intervals using the summary function and you can work with the $boot column manually. Since the AUC is a sample statistic that doesn't depend on the cutpoint estimation, we use the in-bag values in the AUC_b column of the bootstrap results.

library(cutpointr)
library(tidyverse)
oc <- cutpointr(data = suicide, x = dsi, class = suicide, subgroup = gender,
                boot_runs = 1000, break_ties = mean)
#> Assuming the positive class is yes
#> Assuming the positive class has higher x values
#> Running bootstrap...

## Example 1:
summary(oc)
#> Method: maximize_metric 
#> Predictor: dsi 
#> Outcome: suicide 
#> Direction: >= 
#> Subgroups: female, male 
#> Nr. of bootstraps: 1000 
#> 
#> Subgroup: female 
#> --------------------------------------------------------------------------- 
#>  optimal_cutpoint sum_sens_spec    acc sensitivity specificity    AUC
#>                 2        1.8081 0.8852      0.9259      0.8822 0.9446
#>  n_pos n_neg
#>     27   365
#> 
#> Cutpoint 2:
#>           observation
#> prediction yes  no
#>        yes  25  43
#>        no    2 322
#> 
#> 
#> Predictor summary: 
#>  Min. 5% 1st Qu. Median   Mean 3rd Qu. 95% Max.     SD
#>     0  0       0      0 0.8393       1   5   10 1.7452
#> 
#> Predictor summary per class: 
#>     Min.  5% 1st Qu. Median   Mean 3rd Qu. 95% Max     SD
#> no     0 0.0       0      0 0.5479       0   4  10 1.3181
#> yes    0 1.3       4      5 4.7778       6   7   9 2.0444
#> 
#> Bootstrap summary: 
#>           Variable   Min.     5% 1st Qu. Median   Mean 3rd Qu.    95%
#>   optimal_cutpoint 1.0000 1.0000  2.0000 2.0000 2.1540  2.0000 4.0000
#>              AUC_b 0.8446 0.9023  0.9315 0.9497 0.9458  0.9643 0.9777
#>            AUC_oob 0.7935 0.8848  0.9163 0.9514 0.9420  0.9695 0.9845
#>    sum_sens_spec_b 1.6244 1.7210  1.7838 1.8194 1.8157  1.8516 1.8904
#>  sum_sens_spec_oob 1.3064 1.6051  1.7231 1.7796 1.7739  1.8540 1.9041
#>              acc_b 0.7628 0.8162  0.8750 0.8878 0.8851  0.9005 0.9260
#>            acc_oob 0.7338 0.8200  0.8676 0.8844 0.8807  0.9014 0.9231
#>      sensitivity_b 0.7692 0.8499  0.9062 0.9375 0.9342  0.9655 1.0000
#>    sensitivity_oob 0.4286 0.6921  0.8462 0.9000 0.8941  1.0000 1.0000
#>      specificity_b 0.7513 0.8059  0.8707 0.8840 0.8815  0.8986 0.9244
#>    specificity_oob 0.7154 0.8045  0.8632 0.8828 0.8798  0.9025 0.9286
#>            kappa_b 0.2101 0.3435  0.4353 0.4847 0.4812  0.5352 0.5971
#>          kappa_oob 0.1035 0.2860  0.3945 0.4589 0.4533  0.5211 0.6037
#>    Max.     SD
#>  4.0000 0.6803
#>  0.9882 0.0235
#>  0.9991 0.0335
#>  1.9412 0.0510
#>  1.9470 0.0948
#>  0.9541 0.0290
#>  0.9496 0.0322
#>  1.0000 0.0472
#>  1.0000 0.1015
#>  0.9533 0.0315
#>  0.9580 0.0367
#>  0.7261 0.0790
#>  0.7112 0.0948
#> 
#> Subgroup: male 
#> --------------------------------------------------------------------------- 
#>  optimal_cutpoint sum_sens_spec    acc sensitivity specificity    AUC
#>                 3        1.6251 0.8429      0.7778      0.8473 0.8617
#>  n_pos n_neg
#>      9   131
#> 
#> Cutpoint 3:
#>           observation
#> prediction yes  no
#>        yes   7  20
#>        no    2 111
#> 
#> 
#> Predictor summary: 
#>  Min. 5% 1st Qu. Median Mean 3rd Qu. 95% Max.     SD
#>     0  0       0      0 1.15       1   6   11 2.1151
#> 
#> Predictor summary per class: 
#>     Min.  5% 1st Qu. Median   Mean 3rd Qu.  95% Max     SD
#> no     0 0.0       0      0 0.8702       1  5.0   6 1.6286
#> yes    0 0.4       3      4 5.2222       8 10.6  11 3.8333
#> 
#> Bootstrap summary: 
#>           Variable    Min.      5% 1st Qu. Median   Mean 3rd Qu.    95%
#>   optimal_cutpoint  1.0000  1.0000  2.0000 3.0000 2.9785  4.0000 6.0000
#>              AUC_b  0.5381  0.7287  0.8176 0.8673 0.8614  0.9163 0.9630
#>            AUC_oob  0.3333  0.6749  0.7999 0.8894 0.8663  0.9440 0.9965
#>    sum_sens_spec_b  1.1504  1.4392  1.5877 1.6693 1.6649  1.7462 1.8722
#>  sum_sens_spec_oob  0.7925  0.9771  1.3333 1.5064 1.4879  1.6667 1.8680
#>              acc_b  0.6000  0.6714  0.7786 0.8500 0.8266  0.8786 0.9571
#>            acc_oob  0.5556  0.6481  0.7678 0.8302 0.8101  0.8679 0.9153
#>      sensitivity_b  0.2857  0.5833  0.7500 0.8571 0.8389  1.0000 1.0000
#>    sensitivity_oob  0.0000  0.0000  0.5000 0.6667 0.6662  1.0000 1.0000
#>      specificity_b  0.5769  0.6616  0.7710 0.8496 0.8260  0.8798 0.9695
#>    specificity_oob  0.5556  0.6327  0.7660 0.8438 0.8217  0.8873 0.9608
#>            kappa_b  0.0594  0.1354  0.2337 0.3272 0.3365  0.4143 0.5895
#>          kappa_oob -0.0833 -0.0325  0.1521 0.2354 0.2409  0.3285 0.4632
#>     Max.     SD
#>  11.0000 1.5067
#>   1.0000 0.0727
#>   1.0000 0.1019
#>   2.0000 0.1287
#>   1.9412 0.2552
#>   1.0000 0.0797
#>   0.9615 0.0820
#>   1.0000 0.1355
#>   1.0000 0.2915
#>   1.0000 0.0878
#>   1.0000 0.0986
#>   1.0000 0.1399
#>   0.7273 0.1369

## Example 2:
quantile(oc$boot[[1]]$AUC_b, probs = c(0.01, 0.05, 0.95, 0.99))
#>        1%        5%       95%       99% 
#> 0.8829067 0.9022733 0.9776727 0.9836081

## Example 3:
map2(oc$subgroup, oc$boot, function(g, b) {
    l <- list(summary(b$AUC_b))
    names(l) <- g
    return(l)
})
#> [[1]]
#> [[1]]$female
#>    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
#>  0.8446  0.9315  0.9497  0.9458  0.9643  0.9882 
#> 
#> 
#> [[2]]
#> [[2]]$male
#>    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
#>  0.5381  0.8176  0.8673  0.8614  0.9163  1.0000

^{Created on 2018-11-28 by the reprex package (v0.2.1)}

[andresimi]

Thank you again!

[Thie1e]

You're welcome, thanks for reporting issues.