Type:	Package
Title:	Alternative and Fast ROC Analysis
Version:	1.0.0
Date:	2025-06-27
Description:	Alternative and fast algorithms for the analysis of receiver operating characteristics curves (ROC curves) as described in Thomas et al. (2017) <doi:10.1186/s41512-017-0017-y> and Thomas et al. (2023) <doi:10.1016/j.ajogmf.2023.101110>.
Depends:	R (≥ 4.0.0), stats,utils, graphics, pROC, plyr, sn, Hmisc
Suggests:	roxygen2 (≥ 3.1.0), testthat
Imports:	Rcpp (≥ 1.0.9)
LinkingTo:	Rcpp
License:	GPL-3
Encoding:	UTF-8
RoxygenNote:	7.3.2
URL:	https://bitbucket.org/SQ4/alternativeROC
ByteCompile:	true
Repository:	CRAN
BugReports:	https://bitbucket.org/SQ4/alternativeROC/issues
NeedsCompilation:	yes
Packaged:	2025-06-29 06:28:32 UTC; GregoireThomas
Author:	Gregoire Thomas [aut, cre], Robin Tuytten [ctb], Jef Moerman [ctb], Xavier Robin [cph] (Co-author of delong.cpp from package pROC), Stefan Siegert [cph] (Co-author of delong.cpp from package pROC)
Maintainer:	Gregoire Thomas <gregoire.thomas@SQU4RE.com>
Date/Publication:	2025-06-29 06:50:02 UTC

alternativeROC: Alternative and Fast ROC Analysis

Description

Alternative and fast algorithms for the analysis of receiver operating characteristics curves (ROC curves) as described in Thomas et al. (2017) doi:10.1186/s41512-017-0017-y and Thomas et al. (2023) doi:10.1016/j.ajogmf.2023.101110.

Author(s)

Maintainer: Gregoire Thomas gregoire.thomas@SQU4RE.com (ORCID)

Other contributors:

• Robin Tuytten robin.tuytten@metabolomicdiagnostics.com (ORCID) [contributor]

• Jef Moerman [contributor]

• Xavier Robin (Co-author of delong.cpp from package pROC) [copyright holder]

• Stefan Siegert (Co-author of delong.cpp from package pROC) [copyright holder]

See Also

Useful links:

• https://bitbucket.org/SQ4/alternativeROC

• Report bugs at https://bitbucket.org/SQ4/alternativeROC/issues

alternativeROC

Description

Alternative analysis methods for the analysis of receiver operating characteristics curve (ROC curve) as described in Thomas et al. (2017) <doi:10.1186/s41512-017-0017-y>.

Author(s)

Gregoire Thomas <gregoire.thomas@SQU4RE.com>

Arguments used across the functions of the alternativeROC package.

Description

Arguments used across the functions of the alternativeROC package.

Arguments

Value

No return value, used for the documentation of the functions of the package.

Diagnostic performance: Confidence interval for negative predictive value

Description

This function computes a confidence interval for the negative predictive value from a ROC curve, given the prevalence of the negative outcome.

Usage

cinpv(roc, npv, prevalence, boot.n, quantiles = c(0.5, 0.025, 0.975), ...)

Arguments

Details

This function computes a confidence interval for the negative predictive value from a ROC curve, given the prevalence of the negative outcome. The confidence interval is computed using bootstrap resampling.

Value

A numeric vector of length 3 containing the median, lower bound, and upper bound of the confidence interval.

Diagnostic performance: Confidence interval for positive predictive value

Description

This function computes a confidence interval for the positive predictive value from a ROC curve, given the prevalence of the positive outcome.

Usage

cippv(
  roc,
  ppv,
  prevalence,
  boot.n = 2000,
  quantiles = c(0.5, 0.025, 0.975),
  ...
)

Arguments

Details

This function computes a confidence interval for the positive predictive value from a ROC curve, given the prevalence of the positive outcome. The confidence interval is computed using bootstrap resampling.

Value

A numeric vector of length 3 containing the median, lower bound, and upper bound of the confidence interval.

Diagnostic performance: Negative predictive values from a ROC curve

Description

Diagnostic performance: Negative predictive values (NPV) from a ROC curve

Usage

npv(roc, prevalence)

Arguments

Value

A matrix with the following columns:

• threshold: The thresholds used to compute the sensitivity and specificity.

• sensitivity: The sensitivities at the threshold.

• specificity: The specificities at the threshold.

• npv: The negative predictive values at the threshold.

• prevalence: The prevalence of the endpoint in the study population, as provided in the input.

Diagnostic performance: Sensitivity from specificity, NPV and prevalence

Description

Diagnostic performance: Sensitivity from specificity, negative predictive value (NPV) and prevalence of the endpoint

Usage

npv.seatsp(sp, npv, prevalence)

Arguments

Value

Sensitivity from specificity, NPV and prevalence.

Diagnostic performance: Specificity from sensitivity, NPV and prevalence

Description

Diagnostic performance: Specificity from sensitivity, negative predictive value (NPV) and prevalence of the endpoint

Usage

npv.spatse(se, npv, prevalence)

Arguments

Value

Specificity from sensitivity, NPV and prevalence.

p[AUC=0.5]

Description

p[AUC=0.5] using DeLong's methods (DeLong et al. 1988).

Usage

p.auc(roc, ref = 0.5)

Arguments

Value

p value.

Diagnostic performance: Plot ROC curve

Description

Diagnostic performance: Plot ROC curve

Usage

plotROC(
  x,
  annotate = FALSE,
  col.diagonal = "#00000080",
  lty.diagonal = "solid",
  lwd.diagonal = 1,
  col = "#303030",
  lwd = 2,
  cex = 2,
  ppv = NULL,
  npv = NULL,
  prevalence = NULL,
  col.pvs = "#888888",
  col.ci = "#dd0000",
  lwd.ci = lwd,
  len.ci = 0.1,
  boot.n = 1000,
  conf.level = 0.95,
  ...
)

Arguments

Value

A list with the following elements:

• AUC: A numeric vector of length 3 containing the median, lower bound, and upper bound of the AUC.

• PPV: The positive predictive value cutoff.

• sensitivity@PPV: A numeric vector of length 3 containing the median, lower bound, and upper bound of the sensitivity at the specified PPV.

• NPV: The negative predictive value cutoff.

• specificity@NPV: A numeric vector of length 3 containing the median, lower bound, and upper bound of the specificity at the specified NPV.

Examples

set.seed(0)
count.neg <- count.pos <- 200
sd.neg <- sd.pos <- 1
mean.neg <- .5
mean.pos <- 1
skew.neg <- -8
skew.pos <- 0
x <- c(sn::rsn(count.neg,mean.neg,sd.neg,skew.neg),
       sn::rsn(count.pos,mean.pos,sd.pos,skew.pos))
y <- ordered(c(rep("neg",count.neg),rep("pos",count.pos)),levels=c("neg","pos"))
r1 <- roc(y,x)
plotROC(r1,ppv=0.2,prevalence=0.05,boot.n=1e2)
skew.neg <- 0
skew.pos <- 8
x <- c(sn::rsn(count.neg,mean.neg,sd.neg,skew.neg),
       sn::rsn(count.pos,mean.pos,sd.pos,skew.pos))
y <- ordered(c(rep("neg",count.neg),rep("pos",count.pos)),levels=c("neg","pos"))
r2 <- roc(y,x)
plotROC(r2,npv=0.995,prevalence=0.05,boot.n=1e2)

Diagnostic performance: Positive predictive values from a ROC curve

Description

Diagnostic performance: Positive predictive values (PPV) from a ROC curve

Usage

ppv(roc, prevalence)

Arguments

Value

A matrix with the following columns:

• threshold: The thresholds used to compute the sensitivity and specificity.

• sensitivity: The sensitivities at the threshold.

• specificity: The specificities at the threshold.

• ppv: The positive predictive values at the threshold.

• prevalence: The prevalence of the endpoint in the study population, as provided in the input.

Diagnostic performance: Sensitivity from specificity, PPV and prevalence

Description

Diagnostic performance: Sensitivity from specificity, positive predictive value (PPV) and prevalence of the endpoint

Usage

ppv.seatsp(sp, ppv, prevalence)

Arguments

Value

Sensitivity from specificity, PPV and prevalence.

Diagnostic performance: Specificity from sensitivity, PPV and prevalence

Description

Diagnostic performance: Specificity from sensitivity, positive predictive value (PPV) and prevalence of the endpoint

Usage

ppv.spatse(se, ppv, prevalence)

Arguments

Value

Specificity from sensitivity, PPV and prevalence.

Diagnostic performance: Predictive values from a ROC curve

Description

Diagnostic performance: Predictive values from a ROC curve

Usage

pvs(
  roc = NULL,
  prevalence,
  thresholds = roc$thresholds,
  sensitivities = roc$sensitivities,
  specificities = roc$specificities
)

Arguments

Details

This function computes the positive and negative predictive values from a ROC curve, given the prevalence of the positive outcome.

Value

A data frame with the following columns:

• threshold: Thresholds of the ROC curve.

• sensitivity: Sensitivity values of the ROC curve.

• specificity: Specificity values of the ROC curve.

• prevalence: Prevalence of the positive outcome.

• ppv: Positive predictive value.

• npv: Negative predictive value.

ROC curve performances

Description

Range of statistics associated with a ROC curve with confidence interval where applicable. This function is faster than the alternatives provided by the package pROC.

Usage

rocperf(
  x,
  y,
  sensitivities = NULL,
  specificities = NULL,
  conf.level = 0.95,
  fun = NULL,
  seed = 1,
  boot.n = 2000,
  median = FALSE,
  attr = FALSE,
  parallel = FALSE,
  simplify = TRUE,
  ...
)

Arguments

Details

This function computes the area under the ROC curve (AUC) and its confidence interval, the Mann-Whitney U test p-value, and the p-value for the null hypothesis that the AUC is equal to 0.5 (DeLong et al. 1988).

The function also computes the sensitivity at specified specificities and the specificity at specified sensitivities, with confidence intervals and interquartile ranges if bootstrapping is performed.

The function uses the pROC package to compute the ROC curve and confidence intervals, and it can handle parallel processing for bootstrapping.

The function returns a data frame with the computed statistics, including:

• Number of control and case patients

• Mann Whitney U test p-value

• AUC and its confidence intervals

• Sensitivity at specified specificities and their confidence intervals

• Specificity at specified sensitivities and their confidence intervals

The function fun must take the following arguments:

• controls: vector of control values

• cases: vector of case values

• thresholds: vector of thresholds used for the ROC curve

• sensitivities: vector of sensitivities

• specificities: vector of specificities

• ...: additional arguments

and return a named vector of values.

Value

A data frame with the following columns:

• n.control: Number of control patients

• n.case: Number of case patients

• MannWhitney.pvalue: Mann Whitney U test p-value

• AUC.pvalue: p-value for the null hypothesis that AUC=0.5

• AUC: Area under the ROC curve (point estimate)

• AUC.lCI: Lower limit of 95% confidence interval for AUC

• AUC.uCI: Upper limit of 95% confidence interval for AUC

• AUC.lQuart: Lower limit of 50% confidence interval for AUC

• AUC.uQuart: Upper limit of 50% confidence interval for AUC

• Se@SpX: Sensitivity at X% specificity

• Se@SpX.lCI: Lower limit of 95% confidence interval for sensitivity at X% specificity

• Se@SpX.uCI: Upper limit of 95% confidence interval for sensitivity at X% specificity

• Se@SpX.lQuart: Lower limit of 95% confidence interval for sensitivity at X% specificity

• Se@SpX.uQuart: Upper limit of 95% confidence interval for sensitivity at X% specificity

• Sp@SeX: Specificity at X% sensitivity

• Sp@SeX.lCI: Lower limit of 95% confidence interval for specificity at X% sensitivity

• Sp@SeX.uCI: Upper limit of 95% confidence interval for specificity at X% sensitivity

• Sp@SeX.lQuart: Lower limit of 50% confidence interval for specificity at X% sensitivity

• Sp@SeX.uQuart: Upper limit of 50% confidence interval for specificity at X% sensitivity

• Additional columns for statistics computed by the function fun if provided

data.frame with one row with computed statistics in columns.

Examples

fu <- function(controls,cases,thr,se,sp,...) {
  r <- pROC::roc(c(rep(0,length(controls)),
                   rep(1,length(cases))),
                 c(controls,cases),
                 quiet=TRUE)
  c(AUC_fun=r$auc) 
}
set.seed(1)
n <- 123
y <- runif(n)<.5
x <- rnorm(n)+y*1
ans <- rocperf(x,y,fun=fu)
ans <- rocperf(x,y,fun=fu,
               senitivities=c(.5,.75,.9),
               specificities=c(.5,.75,.9))
               

rocthreshold

Description

Compute ROC sensitivity, specificity and threshold for a given cutoff.

Usage

rocthreshold(x, y, cut, input, prevalence = NULL, annotate = FALSE)

Arguments

Details

This function computes the sensitivity, specificity, threshold, PPV and NPV at a given cutoff value from a ROC curve.

NPV and PPV are computed only if a prevalence is provided as input.

If the ROC curve is not provided, it will be computed from the predictor and outcome.

Value

a vector with sensitivity, specificity, threshold, PPV and NPV at the given cutoff.