clinfun (version 1.0.15)

roc.perm.test: Permutation test to compare ROC curve

Description

Computes the test statistic and permutation reference distribution for comparing paired or unpaired ROC curves.

Usage

roc.perm.test(marker, status, marker2=NULL, group=NULL,
              nperm=2500, mp=NULL)
# S3 method for roc.perm.test
print(x, …)
# S3 method for roc.perm.test
plot(x, …)

Arguments

marker

marker values for each subject.

status

binary disease status indicator.

marker2

second diagnostic marker for the same subjects (paired).

group

indicator of which diagnostic test was used (unpaired).

nperm

number of permutations for the reference distribution.

mp

mixing proportion for the unpaired case when proportion of diseased subjects can differ.

x

object of class roc.perm.test output from this function.

...

optional arguments to print and plot functions.

Value

an object of class roc.perm.test with the following elements

ostat

test statistic from the observed data.

pstat

test statistic from permuted data.

p.value

the p-value for the test of equality (2-sided).

The "print" method formats and returns the statistic and p-value. The "plot" method plots the density from the permutation reference distribution and marks the location of the observed statistic.

Details

This function implements the permutation method described in the Venkatraman and Begg (1996) paper for the paired case and the Venkatraman (2000) paper for the unpaired case.

The function detects whether the data are paired or unpaired by testing which of the options marker2 and group is specified. If both are missing it will stop with an error message. At present exactly one should be missing.

References

Venkatraman, E.S. and Begg, C.B. (1996). A distribution-free procedure for comparing receiver operating characteristic curves from a paired experiment. Biometrika 83, 835-848.

Venkatraman, E.S. (2000) A permutation test to compare receiver operating characteristic curves. Biometrics 56(4):1134-8.

Examples

# NOT RUN {
d <- rep(0:1, 50)
x <- rnorm(100) + 1.2*d
y <- rnorm(100) + 1.2*d
oo <- roc.perm.test(x, d, marker2=y)
plot(oo)
oo <- roc.perm.test(c(x,y), c(d,d), group=rep(1:2,each=100))
plot(oo)
# }