sesp.diff.ci {DTComPair} | R Documentation |
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
tab |
An object of class |
ci.method |
The available methods are “ |
alpha |
Significance level alpha for 100(1-alpha)%-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 |
Details
For details and recommendations see Newcombe (2012) and Wenzel and Zapf (2013).
Value
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(1-alpha)%-confidence intervals. |
cont.corr |
A logical value indicating whether the continuity correction was applied. |
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")