AROC (version 1.0-4)

pooledROC.emp: Empirical estimation of the pooled ROC curve.

Description

Estimates the pooled ROC curve using the empirical estimator proposed by Hsieh and Turnbull (1996).

Usage

pooledROC.emp(y0, y1, p = seq(0, 1, l = 101), B = 500, 
	method = c("ncoutcome", "coutcome"))

Arguments

y0

Diagnostic test outcomes in the healthy group.

y1

Diagnostic test outcomes in the diseased group.

p

Set of false positive fractions (FPF) at which to estimate the covariate-adjusted ROC curve.

B

An integer value specifying the number of bootstrap resamples for the construction of the confidence intervals. By default 500.

method

A character string specifying if bootstrap resampling (for the confidence intervals) should be done with or without regard to the disease status (``coutcome'' or ``noutcome''). In both cases, a naive bootstrap is used. By default, the resampling is done conditionally on the disease status.

Value

As a result, the function provides a list with the following components:

call

the matched call.

p

Set of false positive fractions (FPF) at which the pooled ROC curve has been estimated

ROC

Estimated pooled ROC curve, and corresponding 95% confidence intervals (if required)

AUC

Estimated pooled AUC, and corresponding 95% confidence intervals (if required).

References

Hsieh, F., and Turnbull, B.W. (1996). Nonparametric and semiparametric estimation of the receiver operating characteristic curve, The Annals of Statistics, 24, 25-40.

See Also

AROC.bnp, AROC.bsp, AROC.sp, AROC.kernel, pooledROC.BB or pooledROC.emp.

Examples

# NOT RUN {
library(AROC)
data(psa)
# Select the last measurement
newpsa <- psa[!duplicated(psa$id, fromLast = TRUE),]

# Log-transform the biomarker
newpsa$l_marker1 <- log(newpsa$marker1)
# }
# NOT RUN {
m0_emp <- pooledROC.emp(newpsa$l_marker1[newpsa$status == 0],
newpsa$l_marker1[newpsa$status == 1], p = seq(0,1,l=101), B = 500)

summary(m0_emp)

plot(m0_emp)
# }