Type:	Package
Title:	Comparison of Binary Diagnostic Tests in a Paired Study Design
Version:	1.2.6
Description:	Comparison of the accuracy of two binary diagnostic tests in a "paired" study design, i.e. when each test is applied to each subject in the study.
License:	GPL (≥ 3)
URL:	https://github.com/chstock/DTComPair, https://chstock.github.io/DTComPair/
BugReports:	https://github.com/chstock/DTComPair/issues
Encoding:	UTF-8
LazyData:	true
Imports:	gee, ellipse, stats, assertthat
Depends:	R (≥ 3.5.0), PropCIs
Suggests:	knitr, rmarkdown, spelling
RoxygenNote:	7.3.1
Language:	en-US
NeedsCompilation:	no
Packaged:	2024-09-24 15:33:49 UTC; stockch
Author:	Christian Stock [aut, cre], Thomas Hielscher [aut], Andrea Discacciati [aut]
Maintainer:	Christian Stock <christian.stock@boehringer-ingelheim.com>
Repository:	CRAN
Date/Publication:	2024-09-24 15:50:02 UTC

Comparison of Binary Diagnostic Tests in a Paired Study Design

Description

Comparison of the accuracy of two binary diagnostic tests in a “paired” study design, i.e. when each test is applied to each subject in the study.

Details

The accuracy measures that can be compared in the present version are sensitivity, specificity, positive and negative predictive values, and positive and negative diagnostic likelihood ratios.

It is required that results from a binary gold-standard test are also available.

Methods for comparison of sensitivity and specificity: McNemar test (McNemar, 1947) and exact binomial test. Further, several methods to compute confidence intervals for differences in sensitivity and specificity are implemented.

Methods for comparison of positive and negative predictive values: generalized score statistic (Leisenring, Alonzo and Pepe, 2000), weighted generalized score statistic (Kosinski, 2013) and comparison of relative predictive values (Moskowitz and Pepe, 2006).

Methods for comparison of positive and negative diagnostic likelihood ratios: a regression model approach (Gu and Pepe, 2009).

For a general introduction into the evaluation of diagnostic tests see e.g. Pepe (2003), Zhou, Obuchowski and McClish (2011).

Author(s)

Christian Stock, Thomas Hielscher and Andrea Discacciati

Maintainer: Christian Stock <christian.stock@boehringer-ingelheim.com>

References

Gu and Pepe (2009), "Estimating the capacity for improvement in risk prediction with a marker", <doi:10.1093/biostatistics/kxn025>.

Kosinski (2013), "A weighted generalized score statistic for comparison of predictive values of diagnostic tests", <doi:10.1002/sim.5587>.

Leisenring, Alonzo and Pepe (2000), "Comparisons of predictive values of binary medical diagnostic tests for paired designs", <doi:10.1111/j.0006-341X.2000.00345.x>.

McNemar (1947), "Note on the sampling error of the difference between correlated proportions or percentages", <doi:10.1007/BF02295996>.

Moskowitz and Pepe (2006), "Comparing the predictive values of diagnostic tests: sample size and analysis for paired study designs", <doi:10.1191/1740774506cn147oa>.

Pepe (2003, ISBN:978-0198509844), "The statistical evaluation of medical tests for classification and prediction".

Zhou, Obuchowski and McClish (2011), "Statistical Methods in Diagnostic Medicine", <doi:10.1002/9780470906514>.

See Also

Data management functions: tab.1test, tab.paired, read.tab.paired, generate.paired and represent.long.

Computation of standard accuracy measures for a single test: acc.1test and acc.paired.

Comparison of sensitivity and specificity: sesp.mcnemar, sesp.exactbinom and sesp.diff.ci.

Comparison of positive and negative predictive values: pv.gs, pv.wgs and pv.rpv.

Comparison of positive and negative diagnostic likelihood ratios: dlr.regtest and DLR.

Examples

data(Paired1) # Hypothetical study data 
hsd <- tab.paired(d=d, y1=y1, y2=y2, data=Paired1)
acc.paired(hsd)
sesp.mcnemar(hsd)
pv.rpv(hsd)
dlr.regtest(hsd)

Estimating the Capacity for Improvement in Diagnostic Risk Prediction with an additional marker based on the Diagnostic Likelihood Ratio (DLR)

Description

This function allows for estimating the log diagnostic likelihood ratio in a regression model approach. It can be used to assess the gain in diagnostic accuracy for a new binary or continuous diagnostic marker compared to established markers, to determine the impact of covariates on the risk prediction model, and to estimate the DLR for selected marker/covariate values.

Usage

DLR(basemodel, augmentedmodel, diseasestatus, dataset, clustervar = NULL, alpha=0.05)

Arguments

Details

This function is an implementation of the algorithm described in the appendix of Gu and Pepe (2009) using the GEE approach in order to get standard error estimates. The definition of I and Zero matrices is slightly more flexible than the ones described in section 3 in order to allow for models without interaction.

Value

Returns a list including

Author(s)

Thomas Hielscher (t.hielscher@dkfz.de)

References

Gu, W. and Pepe, M. S. (2009). Estimating the capacity for improvement in risk prediction with a marker. Biostatistics, 10(1):172-86.

See Also

dlr.regtest

Examples

library(DTComPair)
data(Paired1)

# test y1 conditioned on null model: DLR+(Y1=1) and DLR-(Y1=0)

DLR("~ 1","~ y1","d",Paired1)

# test y1 conditioned on test y2 with interaction, DLR+(Y1=1|Y2=1) and DLR-(Y1=0|Y2=1)

DLR("~ y2","~ y2 * y1","d",Paired1) 

DTComPair-dataset 1

Description

Hypothetical data from a paired study that aims to compare the accuracy of two binary diagnostic tests.

Usage

Paired1

Format

A dataframe containing 3 columns (d, y1 and y2) and 712 rows.

d is a numeric vector specifying the gold-standard results (1 = presence of disease, 0 = absence of disease).

y1 is a numeric vector specifying the results of diagnostic test 1 (1 = positive, 0 = negative).

y2 is a numeric vector specifying the results of diagnostic test 1 (1 = positive, 0 = negative).

Examples

data(Paired1) # Hypothetical study data 
ftable(Paired1)

Accuracy of a Single Binary Diagnostic Test

Description

Sensitivity and specificity, (positive and negative) predictive values and (positive and negative) diagnostic likelihood ratios of a single binary diagnostic test.

Usage

acc.1test(tab, alpha, testname, method.ci, ...)

Arguments

Details

The calculation of accuracy measures follows standard methodology, e.g. described in Pepe (2003) or Zhou et al. (2011).

The confidence intervals for sensitivity, specificity, and predictive values are computed using the methodology implemented in the function passed to the argument method.ci.

Available options are:

1. waldci - Wald asymptotic normal-based confidence interval; the default,

2. logitci - asymptotic normal-based confidence interval on the logit scale and then back-transformed,

3. exactci - Clopper-Pearson exact confidence interval,

4. add4cici - Agresti-Coull add-4 confidence interval,

5. addz2ci - Agresti-Coull add-z^2/2 confidence interval,

6. blakerci - Blaker exact confidence interval,

7. scoreci - Wilson score confidence interval,

8. midPci - mid-P confidence interval.

Options (3) to (8) are based on the homonymous functions from the {PropCIs} package. Please see the respective package documentation for more details.

Confidence intervals for diagnostic likelihood ratios are computed according to Simel et al. (1991).

Value

A list of class acc.1test:

References

Pepe, M. (2003). The statistical evaluation of medical tests for classification and prediction. Oxford Statistical Science Series. Oxford University Press, 1st edition.

Simel, D.L., Samsa, G.P., Matchar, D.B. (1991). Likelihood ratios with confidence: sample size estimation for diagnostic test studies. J Clin Epidemiol, 44(8):763-70.

Zhou, X., Obuchowski, N., and McClish, D. (2011). Statistical Methods in Diagnostic Medicine. Wiley Series in Probability and Statistics. John Wiley & Sons, Hoboken, New Jersey, 2nd edition.

See Also

tab.1test, print.acc.1test, acc.paired.

Examples

data(Paired1) # Hypothetical study data 
a1 <- tab.1test(d=d, y=y1, data=Paired1)
a2 <- acc.1test(a1)
print(a2)
a3 <- acc.1test(a1, method="exactci", conf.level=0.99)
print(a3)

Accuracy of Two Binary Diagnostic Tests in a Paired Study Design

Description

Sensitivity and specificity, (positive and negative) predictive values and (positive and negative) diagnostic likelihood ratios of a two binary diagnostic tests in a paired study design.

Usage

acc.paired(tab, alpha, method.ci, ...)

Arguments

Details

The calculation of accuracy measures follows standard methodology, e.g. described in Pepe (2003) or Zhou et al. (2011).

The confidence intervals for sensitivity, specificity, and predictive values are computed using the methodology implemented in the function passed to the argument method.ci.

Confidence intervals for diagnostic likelihood ratios are computed according to Simel et al. (1991).

Value

A list of class acc.paired:

References

Pepe, M. (2003). The statistical evaluation of medical tests for classifcation and prediction. Oxford Statistical Science Series. Oxford University Press, 1st edition.

Zhou, X., Obuchowski, N., and McClish, D. (2011). Statistical Methods in Diagnostic Medicine. Wiley Series in Probability and Statistics. John Wiley & Sons, Hoboken, New Jersey, 2nd edition.

See Also

tab.paired, print.acc.paired, acc.1test.

Examples

data(Paired1) # Hypothetical study data 
b1 <- tab.paired(d=d, y1=y1, y2=y2, data=Paired1)
b2 <- acc.paired(b1)
print(b2)

Differences in Diagnostic Likelihood Ratios

Description

Performs a test for differences in (positive and negative) diagnostic likelihood ratios (DLRs) of two binary diagnostic tests in a paired study using a regression model approach proposed by Gu and Pepe (2009).

Usage

dlr.regtest(tab, alpha)

Arguments

Details

The null hypothesis rDLR = DLR of Test 1 / DLR of Test 2 = 1 is tested with respect to both positive and negative DLRs of the two diagnostic tests.

This function calls DLR, a general implementation of the method proposed by Gu and Pepe (2009).

Value

A list containing

References

Gu, W. and Pepe, M. S. (2009). Estimating the capacity for improvement in risk prediction with a marker. Biostatistics, 10(1):172-86.

See Also

DLR

Examples

data(Paired1) # Hypothetical study data
ptab <- tab.paired(d=d, y1=y1, y2=y2, data=Paired1)
ptab
dlr.results <- dlr.regtest(ptab)
str(dlr.results)
dlr.results

Elliptical joint confidence region for relative positive and negative predictive value

Description

Returns a 100(1-alpha)% elliptical joint confidence region for the parameter vector {log(relative positive predictive value), log(relative negative predictive value)}.

Usage

ellipse.pv.rpv(x, alpha = 0.05, npoints = 100, exponentiate = FALSE)

Arguments

Value

A list containing:

References

Moskowitz, C.S., and Pepe, M.S. (2006). Comparing the predictive values of diagnostic tests: sample size and analysis for paired study designs. Clin Trials, 3(3):272-9.

See Also

pv.rpv and ellipse::ellipse.

Examples

data(Paired1) # Hypothetical study data
ftable(Paired1)
paired.layout <- tab.paired(d=d, y1=y1, y2=y2, data=Paired1)
paired.layout 
rpv.results <- pv.rpv(paired.layout)
ellipse.data <- ellipse.pv.rpv(rpv.results)
if(interactive()){
  plot(ellipse.data$ellipse, type = "l", ylim = c(-0.4, 0.2), xlim = c(-0.2, 0.2))
  points(ellipse.data$centre[1], ellipse.data$centre[2], col = "red", pch = 19)
  abline(h = 0, v = 0, lty = 3)
}

Generate Dataset from “tab.paired”-Object

Description

Generates a dataset from contingency tables of binary diagnostic test results in a paired study design.

Usage

generate.paired(tab, ...)

Arguments

Value

A dataframe containing:

See Also

tab.paired and read.tab.paired.

Examples

data(Paired1) # Hypothetical study data
ftable(Paired1)
paired.layout <- tab.paired(d=d, y1=y1, y2=y2, data=Paired1)
new.df <- generate.paired(paired.layout)
head(new.df)
ftable(new.df)

Print “acc.1test”-Object

Description

Prints objects of class acc.1test in an easy-to-read form (S3method).

Usage

## S3 method for class 'acc.1test'
print(x, ...)

Arguments

Value

Creates a list object from parts of its input that is then printed in a tabular layout.

See Also

acc.1test.

Examples

data(Paired1) # Hypothetical study data 
a1 <- tab.1test(d=d, y=y1, data=Paired1)
a2 <- acc.1test(a1)
print(a2)

Print “acc.paired”-Object

Description

Prints objects of class acc.paired in an easy-to-read form (S3method).

Usage

## S3 method for class 'acc.paired'
print(x, ...)

Arguments

Value

Creates a list object from parts of its input that is then printed in a tabular layout.

See Also

acc.paired.

Examples

data(Paired1) # Hypothetical study data 
b1 <- tab.paired(d=d, y1=y1, y2=y2, data=Paired1)
b2 <- acc.paired(b1)
print(b2)

Print “tab.1test”-Object

Description

Prints objects of class tab.1test in an easy-to-read form (S3method).

Usage

## S3 method for class 'tab.1test'
print(x, ...)

Arguments

Value

Creates a list object from parts of its input that is then printed in a tabular layout.

See Also

tab.1test, acc.1test, tab.paired.

Examples

data(Paired1) # Hypothetical study data 
a <- tab.1test(d=d, y=y1, data=Paired1)
print(a)

Print “tab.paired”-Object

Description

Prints objects of class tab.paired in an easy-to-read form (S3method).

Usage

## S3 method for class 'tab.paired'
print(x, ...)

Arguments

Value

Creates a list object from parts of its input that is then printed in a tabular layout.

See Also

tab.paired, acc.paired, tab.1test.

Examples

data(Paired1) # Hypothetical study data 
b <- tab.paired(d=d, y1=y1, y2=y2, data=Paired1)
print(b)

Generalized Score Statistic for Comparison of Predictive Values

Description

Performs a test for differences in (positive and negative) predictive values of two binary diagnostic tests using a generalized score statistic proposed by Leisenring, Alonzo and Pepe (2000).

Usage

pv.gs(tab)

Arguments

Value

A list containing:

References

Leisenring, W., Alonzo, T., and Pepe, M. S. (2000). Comparisons of predictive values of binary medical diagnostic tests for paired designs. Biometrics, 56(2):345-51.

See Also

pv.wgs and pv.rpv.

Examples

data(Paired1) # Hypothetical study data
ftable(Paired1)
paired.layout <- tab.paired(d=d, y1=y1, y2=y2, data=Paired1)
paired.layout 
gs.results <- pv.gs(paired.layout)
str(gs.results)
gs.results
gs.results$ppv["p.value"]

Compute predictive values for theoretical prevalences

Description

It is often of interest to estimate predictive values assuming the test were applied to a population with a different prevalence of the disease. Projected predictive values may be calculated using Bayes theorem and the relation between predictive values and diagnostic likelihood ratios can be used to derived corresponding confidence intervals.

Usage

pv.prev(pi, acc)

Arguments

Details

Predictive values, assuming a certain prevalence of the disease, are derived using the relation between predictive values and diagnostic likelihood ratios:

- PPV = 1 / (1 + (1 / pi - 1) / pDLR) - NPV = 1 / (1 + (1 / (1 / pi - 1)) / nDLR).

See Newcombe RG (2013). Confidence Intervals for Proportions and Related Measures of Effect Size. Chapman and Hall/ CRC Biostatistics Series (chapters 12.3+5 and 14.9).

The alpha-level of (1-alpha)-confidence intervals is inherited from 'acc.1test'.

Value

A vector containing the projected values.

See Also

[acc.1test()]

Examples

data(Paired1) # Hypothetical study data 
a1 <- tab.1test(d=d, y=y1, data=Paired1)
a2 <- acc.1test(a1, alpha = 0.05)
pv.prev(pi=0.2, acc=a2)
pv.prev(pi=0.5, acc=a2)

Comparison of Predictive Values using Relative Predictive Values

Description

Performs a test for differences in (positive and negative) predictive values of two binary diagnostic tests in a paired study design using relative predictive values, as proposed by Moskowitz and Pepe (2006).

Usage

pv.rpv(tab, alpha)

Arguments

Value

A list containing:

References

Moskowitz, C.S., and Pepe, M.S. (2006). Comparing the predictive values of diagnostic tests: sample size and analysis for paired study designs. Clin Trials, 3(3):272-9.

See Also

pv.gs and pv.wgs.

Examples

data(Paired1) # Hypothetical study data
ftable(Paired1)
paired.layout <- tab.paired(d=d, y1=y1, y2=y2, data=Paired1)
paired.layout 
rpv.results <- pv.rpv(paired.layout)
str(rpv.results)
rpv.results
rpv.results$ppv["p.value"]

Weighted Generalized Score Statistic for Comparison of Predictive Values

Description

Performs a test for differences in (positive and negative) predictive values of two binary diagnostic tests using a weighted generalized score statistic proposed by Kosinski (2013).

Usage

pv.wgs(tab)

Arguments

Value

A list containing:

References

Kosinski, A.S. (2013). A weighted generalized score statistic for comparison of predictive values of diagnostic tests. Stat Med, 32(6):964-77.

See Also

pv.gs and pv.rpv.

Examples

data(Paired1) # Hypothetical study data
ftable(Paired1)
paired.layout <- tab.paired(d=d, y1=y1, y2=y2, data=Paired1)
paired.layout 
wgs.results <- pv.wgs(paired.layout)
str(wgs.results)
wgs.results
wgs.results$ppv["p.value"]

Read in “tab.1test”-Objects

Description

Reads in objects of class tab.1test using cell frequencies.

Usage

read.tab.1test(a, b, c, d, testname, ...)

Arguments

Value

Returns a list of class tab.1test containing:

Note

Objects of class tab.1test are required as arguments for acc.1test, a function to compute the accuracy of a binary diagnostic test.

See Also

tab.1test, print.tab.1test, acc.1test.

Examples

read.t1 <- read.tab.1test(321, 51, 730, 272, testname="Test1")
class(read.t1)
read.t1
acc.1test(read.t1)

Read in “tab.paired”-Objects

Description

Reads in objects of class tab.paired using cell frequencies.

Usage

read.tab.paired(d.a, d.b, d.c, d.d, nd.a, nd.b, nd.c, nd.d, testnames, ...)

Arguments

Value

Returns a list of class tab.paired containing:

Note

Objects of class tab.paired are essential arguments for various functions in the DTComPair-package.

See Also

tab.paired, print.tab.paired, acc.paired, generate.paired.

Examples

read.t2 <- read.tab.paired(321, 51, 730, 272, 
                           120, 8, 74, 109,
                           testnames=c("Test A", "Test B"))
class(read.t2)
read.t2
acc.paired(read.t2)

Long Representation of Results from Two Binary Diagnostic Tests

Description

Long representation of results from two binary diagnostic tests.

Usage

represent.long(d, y1, y2)

Arguments

Details

Sometimes a long representation of data from a “paired” study of binary diagnostic tests is required, e.g. to run regression analyses.

In a wide representation each subject has 1 record in the dataset containing d, y1 and y2.

In a long representation each subjects has 2 records in the dataset, one for each test. The data format is shown below.

Value

A dataframe containing:

See Also

tab.paired and read.tab.paired.

Examples

data(Paired1) # Hypothetical study data
names(Paired1)
new.long <- represent.long(d=Paired1$d, y1=Paired1$y1, y2=Paired1$y2)
str(new.long)
head(new.long)

Confidence Intervals for Differences in Sensitivity and Specificity

Description

Calculates confidence intervals for differences in sensitivity and specificity of two binary diagnostic tests in a paired study design.

Usage

sesp.diff.ci(tab, ci.method, alpha, cont.corr)

Arguments

Details

For details and recommendations see Newcombe (2012) and Wenzel and Zapf (2013).

Value

A list containing:

References

Altman, D.G. (1991). Practical statistics for medical research. Chapman & Hall, London.

Agresti, A. and Min, Y. (2005). Simple improved confidence intervals for comparing matched proportions. Stat Med, 24(5): 729-40.

Bonett, D.G., and Price, R.M. (2011). Adjusted Wald confidence intervals for a difference of binomial proportions based on paired data. J Educ Behav Stat, 37(4): 479-488.

Newcombe R.G. (2012). Confidence intervals for proportions and related measures of effect size. Chapman and Hall/CRC Biostatistics Series.

Tango, T. (1998). Equivalence test and confidence interval for the difference in proportions for the paired-sample design. Stat Med, 17(8): 891-908.

Wenzel, D., and Zapf, A. (2013). Difference of two dependent sensitivities and specificities: comparison of various approaches. Biom J, 55(5): 705-718.

Examples

library(DTComPair)
t1 <- read.tab.paired(18, 14, 0, 18,
                      18, 12, 2, 18)
t1
sesp.diff.ci(t1, ci.method="wald", cont.corr=FALSE)
sesp.diff.ci(t1, ci.method="wald", cont.corr=TRUE)
sesp.diff.ci(t1, ci.method="agresti-min")
sesp.diff.ci(t1, ci.method="tango")

Exact Binomial Test for Differences in Sensitivity and Specificity

Description

Performs an exact binomial test for differences in sensitivity and specificity of two binary diagnostic tests in a paired study design.

Usage

sesp.exactbinom(tab) 

Arguments

Details

The function performs a standard exact binomial test.

An exact binomial test is recommended when the number of patients with differing results for test 1 and test 2 (discordant pairs) is small, i.e. <20 (Zhou et al., 2011).

Value

A list containing:

References

Zhou, X., Obuchowski, N., and McClish, D. (2011). Statistical Methods in Diagnostic Medicine. Wiley Series in Probability and Statistics. John Wiley & Sons, Hoboken, New Jersey, 2nd edition.

See Also

sesp.mcnemar and tab.paired.

Examples

data(Paired1) # Hypothetical study data
ftable(Paired1)
paired.layout <- tab.paired(d=d, y1=y1, y2=y2, data=Paired1)
paired.layout 
exact.results <- sesp.exactbinom(paired.layout)
str(exact.results)
exact.results
exact.results$sensitivity["p.value"]

Generalized McNemar's test

Description

Performs a generalized McNemar's test to jointly compare sensitivity and specificity.

Usage

sesp.gen.mcnemar(tab)

Arguments

Value

A vector containing the test statistic and the p-value.

References

Lachenbruch P.A., Lynch C.J. (1998). Assessing screening tests: extensions of McNemar's test. Stat Med, 17(19): 2207-17.

See Also

[tab.paired()], [read.tab.paired()] and [sesp.mcnemar()]

Examples

# Example 1:
data(Paired1) # Hypothetical study data
ftable(Paired1)
paired.layout1 <- tab.paired(d=d, y1=y1, y2=y2, data=Paired1)
print(paired.layout1)
sesp.gen.mcnemar(paired.layout1)

# Example 2 (from Lachenbruch and Lynch (1998)):
paired.layout2 <- read.tab.paired(
  d.a  = 850, d.b  = 40, d.c  = 60, d.d  =  50, 
  nd.a =  60, nd.b = 25, nd.c = 15, nd.d = 900, 
  testnames = c("T1", "T2")
)
print(paired.layout2)
sesp.gen.mcnemar(paired.layout2)

McNemar Test for Comparison of Sensitivities and Specificities

Description

Performs a McNemar Test for comparison of sensitivities and specificities of two binary diagnostic tests in a paired study design.

Usage

sesp.mcnemar(tab)

Arguments

Details

The test is performed as described by McNemar (1947).

Value

A list containing:

References

McNemar, Q. (1947). Note on the sampling error of the difference between correlated proportions or percentages. Psychometrika, 12(2):153-7.

See Also

sesp.exactbinom and tab.paired.

Examples

data(Paired1) # Hypothetical study data
ftable(Paired1)
paired.layout <- tab.paired(d=d, y1=y1, y2=y2, data=Paired1)
paired.layout 
mcnem.results <- sesp.mcnemar(paired.layout)
str(mcnem.results)
mcnem.results
mcnem.results$sensitivity["p.value"]

Comparison of the accuracy of two tests using relative sensitivity and specificity

Description

Calculates two-sided Wald confidence intervals and performs a Wald test for the relative sensitivity and specificity of two binary diagnostic tests in a paired study design.

Usage

sesp.rel(tab, alpha)

Arguments

Details

If relative sensitivity>1, the percentage increase in sensitivity for test2 relative to test1 is computed as 100(relative sensitivity-1)%. If relative sensitivity<1 the percentage decrease in sensitivity for test2 relative to test1 is computed as 100(1-relative sensitivity)%. Percentage increase/decrease in specificity is computed in an analogous fashion.

Given the independence of relative sensitivity and relative specificity, a possible joint 100(1-alpha)% confidence region for {relative sensitivity, relative specificity} is formed by the rectangle {lcl.rel.sens, ucl.rel.sens} x {lcl.rel.spec, ucl.rel.spec}, where {lcl.rel.sens, ucl.rel.sens} and {lcl.rel.spec, ucl.rel.spec} are 100(1-alpha*)% confidence intervals for relative sensitivity and relative specificity, respectively, and alpha*=1-sqrt(1-alpha).

The McNemar's test implemented in sesp.mcnemar is asymptotically equivalent to the Wald test implemented here.

Value

A list containing:

References

Alonzo, T. A., Pepe, M. S., & Moskowitz, C. S. (2002). Sample size calculations for comparative studies of medical tests for detecting presence of disease. Statistics in medicine, 21(6), 835-852.

See Also

sesp.diff.ci, sesp.mcnemar, and sesp.exactbinom.

Examples

data(Paired1) # Hypothetical study data
ftable(Paired1)
paired.layout <- tab.paired(d=d, y1=y1, y2=y2, data=Paired1)
paired.layout 
sesp.rel.results <- sesp.rel(paired.layout)
str(sesp.rel.results)
sesp.rel.results

Tabulate Single Binary Diagnostic Test vs. Gold-Standard

Description

Produces a contingency table of results from a single binary diagnostic test vs. the gold-standard results.

Usage

tab.1test(d, y, data = NULL, testname, ...)

Arguments

Value

Returns a list of class tab.1test:

Note

Objects of class tab.1test are required as arguments for acc.1test, a function to compute the accuracy of a binary diagnostic test.

See Also

tab.paired, acc.1test, acc.paired.

Examples

data(Paired1) # Hypothetical study data 
a <- tab.1test(d=d, y=y1, data=Paired1)
str(a)
a$tab.1test
a

Tabulate Results from Two Binary Diagnostic Tests in a Paired Study Design

Description

Produces contingency tables of results from two binary diagnostic tests evaluated in a paired study design.

Usage

tab.paired(d, y1, y2, data = NULL, testnames, ...)

Arguments

Value

Returns a list of class tab.paired:

Note

Objects of class tab.paired are essential arguments for various functions in the DTComPair-package.

See Also

print.tab.paired, read.tab.paired, tab.1test.

Examples

data(Paired1) # Hypothetical study data 
b <- tab.paired(d=d, y1=y1, y2=y2, data=Paired1)
str(b)
b$diseased
b$non.diseased
print(b)

Comparison of the accuracy of two tests using relative true positive and false positive fraction

Description

Calculates two-sided Wald confidence intervals and performs a Wald test for the relative true positive fraction (TPF) (sensitivity) and false positive fraction (FPF) (i.e., one minus specificity) of two binary diagnostic tests in a paired study design.

This function is primarily intended for the analysis of paired screen positive studies, i.e. those paired studies where the disease (outcome) is ascertained using the gold standard test only in subjects who screen positive to either or both diagnostic tests. However, this function can also be used with data from standard paired studies, i.e. where the gold standard test is applied to all subjects.

Usage

tpffpf.rel(tab, alpha)

Arguments

Details

If relative true positive fraction>1, the percentage increase in true positive fraction for Test2 relative to Test1 is computed as 100(relative true positive fraction-1)%. If relative true positive fraction<1 the percentage decrease in true positive fraction for Test2 relative to Test1 is computed as 100(1-relative true positive fraction)%. Percentage increase/decrease in false positive fraction is computed in an analogous fashion.

Given the independence of relative TPR and relative TNR, a possible joint 100(1-alpha)% confidence region for {relative TPF, relative FPF} is formed by the rectangle {lcl.rel.tpf, ucl.rel.tpf} x {lcl.rel.fpf, ucl.rel.fpf}, where {lcl.rel.tpf, ucl.rel.tpf} and {lcl.rel.fpf, ucl.rel.fpf} are 100(1-alpha*)% confidence intervals for relative TPF and relative FPF, respectively, and alpha*=1-sqrt(1-alpha).

In screen positive studies, only relative TPF and relative FPF can be estimated from the data. Their constituents, i.e. TPF and FPF for the two tests, are not estimable. Relative specificity is not estimable either. Therefore, sesp.rel should not be used to attempt to estimate those quantities from studies with a paired screen positive design. McNemar's test (sesp.mcnemar) can, however, be used to test the null hypothesis of equality in specificities in paired screen positive studies (Schatzkin et al., 1987).

Value

A list containing:

References

Schatzkin, A., Connor, R. J., Taylor, P. R., & Bunnag, B. (1987). Comparing new and old screening tests when a reference procedure cannot be performed on all screenees: example of automated cytometry for early detection of cervical cancer. American Journal of Epidemiology, 125(4), 672-678.

Cheng, H., & Macaluso, M. (1997). Comparison of the accuracy of two tests with a confirmatory procedure limited to positive results. Epidemiology, 104-106.

Alonzo, T. A., Pepe, M. S., & Moskowitz, C. S. (2002). Sample size calculations for comparative studies of medical tests for detecting presence of disease. Statistics in medicine, 21(6), 835-852.

See Also

sesp.rel.

Examples

# Data from Cheng and Macaluso (Table 2)
breast.cancer.data <- read.tab.paired(
 10, 24, 21, NA,
 13, 144, 95, NA,
 testnames=c("Mammography", "Physical examination")
)
breast.cancer.data
tpffpf.rel.results <- tpffpf.rel(breast.cancer.data)
str(tpffpf.rel.results)
tpffpf.rel.results