compute.threshold.AROC.sp {AROC}

AROC-based threshold values.

Description

Estimates AROC-based threshold values using the semiparametric approach proposed by Janes and Pepe (2009).

Usage

compute.threshold.AROC.sp(object, newdata, FPF = 0.5)

Arguments

object

An object of class AROC as produced by AROC.sp.

newdata

Data frame with the covariate values at which threshold values are required.

FPF

Numeric vector with the FPF at which to calculate the AROC-based threshold values. Atomic values are also valid.

Details

Estimation of the covariate-adjusted ROC curve (AROC) using the semiparametric approach proposed by Janes and Pepe (2009) involves the estimation of the conditional distribution function for the diagnostic test outcome in the healthy population

F_{\bar{D}}(y|\mathbf{X}_{\bar{D}}) = Pr\{Y_{\bar{D}} \leq y | \mathbf{X}_{\bar{D}}\}.

This function makes use of this estimate in order to calculate AROC-based threshold values. In particular, for a covariate value \mathbf{x} and a FPF = t, the AROC-based threshold value is calculated as follows

\hat{c}_{\mathbf{x}} = \hat{F}^{-1}_{\bar{D}}(1-t|\mathbf{X}_{\bar{D}} = \mathbf{x}).

Value

A matrix with the computed AROC-based threshold values. The matrix has as many columns as different covariate vector values, and as many rows as different FPFs.

References

Janes, H., and Pepe, M.S. (2009). Adjusting for covariate effects on classification accuracy using the covariate-adjusted receiver operating characteristic curve. Biometrika, 96(2), 371 - 382.

See Also

AROC.sp

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)

m3 <- AROC.sp(formula.healthy = l_marker1 ~ age,
group = "status", tag.healthy = 0, data = newpsa,
p = seq(0,1,l=101), B = 500)

FPF = c(0.1, 0.3)
newdata <- data.frame(age = seq(52, 80, l = 50))

th_sp <- compute.threshold.AROC.sp(m3, newdata, FPF)
names(th_sp)