pooledROC.emp: Empirical estimation of the pooled ROC curve.
In AROC: Covariate-Adjusted Receiver Operating Characteristic Curve Inference

pooledROC.emp

R Documentation

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.

Examples

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)

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)

AROC documentation built on March 18, 2022, 5:22 p.m.