"DeLong's test should not be applied to ROC curves with a different direction" about proc HOT 6 CLOSED

library(pROC)
auroc <- read.csv("a.csv", header=TRUE)
score_old_3 <- roc(response = auroc$endpoint, predictor = auroc$score_old_3)
score_new <- roc(response = auroc$endpoint, predictor = auroc$score_new)
roc.test(score_new, score_old_3, method = "delong")

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> library(xlsx)
Loading required package: rJava
Loading required package: xlsxjars
> auroc <- read.xlsx("a.xlsx", 1)
> 
> library(pROC)
Type 'citation("pROC")' for a citation.

Attaching package: ‘pROC’

The following objects are masked from ‘package:stats’:

    cov, smooth, var

> score_old_3 <- roc(response = auroc$endpoint, predictor = auroc$score_old_3)
> print(score_old_3)

Call:
roc.default(response = auroc$endpoint, predictor = auroc$score_old_3)

Data: auroc$score_old_3 in 9 controls (auroc$endpoint 0) < 47 cases (auroc$endpoint 1).
Area under the curve: 0.5662
> score_new <- roc(response = auroc$endpoint, predictor = auroc$score_new)
> print(score_new)

Call:
roc.default(response = auroc$endpoint, predictor = auroc$score_new)

Data: auroc$score_new in 9 controls (auroc$endpoint 0) < 47 cases (auroc$endpoint 1).
Area under the curve: 0.8487
> roc.test(score_new, score_old_3, method = "delong")

	DeLong's test for two correlated ROC curves

data:  score_new and score_old_3
Z = 2.3215, p-value = 0.02026
alternative hypothesis: true difference in AUC is not equal to 0
sample estimates:
AUC of roc1 AUC of roc2 
  0.8486998   0.5661939