sesp.diff.ci {DTComPair}  R Documentation 
Calculates confidence intervals for differences in sensitivity and specificity of two binary diagnostic tests in a paired study design.
sesp.diff.ci(tab, ci.method, alpha, cont.corr)
tab 
An object of class 
ci.method 
The available methods are “ 
alpha 
Significance level alpha for 100(1alpha)%confidence intervals for the difference in sensitivity and specificity, the default is 0.05. 
cont.corr 
A logical value indicating whether the continuity correction should be used (only available for 
For details and recommendations see Newcombe (2012) and Wenzel and Zapf (2013).
A list containing:
sensitivity 
A vector containing

specificity 
A vector containing

ci.method 
The name of the method used to calculate confidence intervals. 
alpha 
The level alpha used to compute 100(1alpha)%confidence intervals. 
cont.corr 
A logical value indicating whether the continuity correction was applied. 
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): 72940.
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): 479488.
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 pairedsample design. Stat Med, 17(8): 891908.
Wenzel, D., and Zapf, A. (2013). Difference of two dependent sensitivities and specificities: comparison of various approaches. Biom J, 55(5): 705718.
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="agrestimin")
sesp.diff.ci(t1, ci.method="tango")