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DTComPair-package R Documentation
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)
DTComPair documentation built on Sept. 24, 2024, 5:08 p.m.
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| DTComPair-package | R Documentation |
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)
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
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