confIntIndependentAUCDiff | R Documentation |
Computes confidence interval for the difference in the area under the curve based on two independent samples.
confIntIndependentAUCDiff(
casesA,
controlsA,
casesB,
controlsB,
type = c("Wald", "logit"),
conf.level = 0.95
)
casesA |
Values of the continuous variable from Test A for the cases. |
controlsA |
Values of the continuous variable from Test A for the controls. |
casesB |
Values of the continuous variable from Test B for the cases. |
controlsB |
Values of the continuous variable from Test B for the controls. |
type |
"Wald" (default) or "logit". |
conf.level |
Confidence level for confidence interval. Default is 0.95. |
For type="Wald", standard Wald confidence intervals are calculated for AUC of both tests and their difference. For type="logit", the substitution method is used based on the logit transformation for the AUC of both tests. The confidence interval for the difference in AUC is then calculated using Newcombe's method.
A data.frame with estimate and confidence limits for AUC of the two tests and their difference.
Leonhard Held
Newcombe, R.G. (1998). Interval estimation for the difference between independent proportions: Comparison of eleven methods. Stat. Med., *17*, 873-890.
Pepe, M.S. (2003) The statistical evaluation of medical tests for classification and prediction. Oxford University Press.
confIntAUC
, confIntPairedAUCDiff
,
standardErrorAUCDiff
set.seed(12345)
casesA <- rnorm(n = 200, mean = 2.5)
controlsA <- rnorm(n = 100)
casesB <- rnorm(n = 100, mean = 1.5)
controlsB <- rnorm(n = 200)
confIntIndependentAUCDiff(casesA = casesA, controlsA = controlsA,
casesB = casesB, controlsB = controlsB, type = "Wald")
confIntIndependentAUCDiff(casesA = casesA, controlsA = controlsA,
casesB = casesB, controlsB = controlsB, type = "logit")
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