roc - 为什么 roc.test() 中 AUC 的估计结果与原始结果不一致？

Question

我做了两条ROC曲线，其中一条结果如下：

> roc1 <- roc(cData$`<60`$resule, cData$`<60`$SOFA1)
Setting levels: control = survival, case = death
Setting direction: controls <cases
> roc1

Call:
roc.default(response = cData$`<60`$resule, predictor = cData$`<60`$SOFA1)

Data: cData$`<60`$SOFA1 in 26 controls (cData$`<60`$resule survival) <18 cases (cData$`<60`$resule death).
Area under the curve: 0.7692

另一个结果如下：

    > roc2 <- roc(cData$`<60`$resule, cData$`<60`$SOFA7)
    Setting levels: control = survival, case = death
    Setting direction: controls <cases
    > roc2



Call:
roc.default(response = cData$`<60`$resule, predictor = cData$`<60`$SOFA7)

Data: cData$`<60`$SOFA7 in 26 controls (cData$`<60`$resule survival) <2 cases (cData$`<60`$resule death).
Area under the curve: 0.9327

roc.test()通过包的比较后得到的结果pROC是：

> roc.test(roc1,roc2)

DeLong's test for two correlated ROC curves

data: roc1 and roc2
Z = 0.54339, p-value = 0.5869
alternative hypothesis: true difference in AUC is not equal to 0
sample estimates:
AUC of roc1 AUC of roc2
  0.9615385 0.9326923

roc1的AUC是0.7692，roc2的AUC是0.9327，但是为什么对比后roc.test()，AUC分别变成0.9615385 0.9326923呢？