roc.area.test: Nonparametric area under the ROC curve

In clinfun: Clinical Trial Design and Data Analysis Functions

Nonparametric area under the ROC curve

Description

Computes the nonparametric area under the ROC curve and its variance based on U-statistic theory (DDCP).

Usage

  roc.area.test(markers, status)
  ## S3 method for class 'roc.area.test'
print(x, ...)

Arguments

markers	The marker values for each subject. If there are more than one markers then this should be a matrix.
status	binary disease status indicator
x	object of class roc.area.test output from this function.
...	optional arguments to the print function.

Details

It calculates the area and its variance. For more than one marker it calculates the statistic to test for the equality of all AUCs. This statistic has a standard normal reference distribution for two variables and chi-square with number of variables minus 1.

Value

a list with the following elements

area	estimated area.
var	estimated variance (matrix).
stat	test statistic for equality of AUCs. Is not returned when only one diagnostic marker is present.
p.value	the p-value for the test of equality (2-sided).
df	the degrees of freedom of the chi-square.

The "print" method formats and returns the output.

References

DeLong, E. R., D. M. DeLong, and D. L. Clarke-Pearson. 1988. Comparing the areas under two or more correlated receiver operating characteristic curves: A nonparametric approach. Biometrics 44:837-845.

Examples

g <- rep(0:1, 50)
x <- rnorm(100) + g
y <- rnorm(100) + g
z <- rnorm(100) + g
roc.area.test(cbind(x,y), g)
roc.area.test(cbind(x,y,z), g)
y1 <- y + 0.75*g
roc.area.test(cbind(x,y1), g)

clinfun documentation built on Oct. 20, 2023, 1:07 a.m.