confIntIndependentAUCDiff {biostatUZH} | 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 = "Wald", 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" or "Logit". Defaults to "Wald". |
conf.level |
Confidence level for confidence interval. |
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 matrix 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: Oxford University Press.
set.seed(12345) casesA <- rnorm(200, mean=2.5) controlsA <- rnorm(100) casesB <- rnorm(100, mean=1.5) controlsB <- rnorm(200) confIntIndependentAUCDiff(casesA, controlsA, casesB, controlsB, type="Wald") confIntIndependentAUCDiff(casesA, controlsA, casesB, controlsB, type="Logit")