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Last data update: 2014.03.03

R: Accuracy of a Single Binary Diagnostic Test

acc.1test

R Documentation

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, ...)

Arguments

`tab`	An object of class `tab.1test`.
`alpha`	Significance level alpha for 100(1-alpha)%-confidence intervals, the default is 0.05.
`testname`	A character variable containing the name of the diagnostic test.
`...`	Additional arguments (usually not required).

Details

The calculation of accuracy measures and their variances follows standard methodology, e.g. described in Pepe (2003) or Zhou et al. (2011). Confidence intervals for diagnostic likelihood ratios are computed according to Simel et al. (1991).

Value

A list of class acc.1test:

tab

A contingency table (matrix) of test results; the same tab that is supplied as an argument.

	Diseased	Non-diseased	Total
Test pos.	...	...	...
Test neg.	...	...	...
Total	...	...	...

sensitivity

A numeric vector containing the estimated sensitivity (est), its standard error (se), lower confidence limit (lcl) and upper confidence limit (ucl).

specificity

A numeric vector containing the estimated specificity (est), its standard error (se), lower confidence limit (lcl) and upper confidence limit (ucl).

ppv

A numeric vector containing the estimated positive predictive value (est), its standard error (se), lower confidence limit (lcl) and upper confidence limit (ucl).

npv

A numeric vector containing the estimated negative predictive value (est), its standard error (se), lower confidence limit (lcl) and upper confidence limit (ucl).

pdlr

A numeric vector containing the estimated positive diagnostic likelihood ratio (est), the standard error of the logarithm of the positive diagnostic likelihood ratio (se.ln), the lower confidence limit (lcl) and the upper confidence limit (ucl).

ndlr

A numeric vector containing the estimated negative diagnostic likelihood ratio (est), the standard error of the logarithm of the negative diagnostic likelihood ratio (se.ln), the lower confidence limit (lcl) and the upper confidence limit (ucl).

alpha

The significance level alpha used to compute 100(1-alpha)%-confidence intervals, the default is 0.05.

testname

A character variable containing the name of the diagnostic test.

References

Pepe, M. (2003). The statistical evaluation of medical tests for classifcation 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.

Examples

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

Results


R version 3.3.1 (2016-06-21) -- "Bug in Your Hair"
Copyright (C) 2016 The R Foundation for Statistical Computing
Platform: x86_64-pc-linux-gnu (64-bit)

R is free software and comes with ABSOLUTELY NO WARRANTY.
You are welcome to redistribute it under certain conditions.
Type 'license()' or 'licence()' for distribution details.

R is a collaborative project with many contributors.
Type 'contributors()' for more information and
'citation()' on how to cite R or R packages in publications.

Type 'demo()' for some demos, 'help()' for on-line help, or
'help.start()' for an HTML browser interface to help.
Type 'q()' to quit R.

> library(DTComPair)
Loading required package: gee
Loading required package: PropCIs
> png(filename="/home/ddbj/snapshot/RGM3/R_CC/result/DTComPair/acc.1test.Rd_%03d_medium.png", width=480, height=480)
> ### Name: acc.1test
> ### Title: Accuracy of a Single Binary Diagnostic Test
> ### Aliases: acc.1test
> 
> ### ** Examples
> 
> data(Paired1) # Hypothetical study data 
> a1 <- tab.1test(d=d, y=y1, data=Paired1)
> a2 <- acc.1test(a1)
> print(a2)
Diagnostic accuracy of test 'y1'

(Estimates, standard errors and 95%-confidence intervals)

                 Est.         SE  Lower CL  Upper CL
Sensitivity 0.8802661 0.01528718 0.8503038 0.9102284
Specificity 0.6781609 0.02891782 0.6214830 0.7348388
PPV         0.8253638 0.01731081 0.7914353 0.8592924
NPV         0.7662338 0.02784617 0.7116563 0.8208113

           Est.  SE (log)  Lower CL  Upper CL
PDLR  2.7351124 0.0915147 2.2860079 3.2724472
NDLR  0.1765568 0.1346088 0.1356142 0.2298601
> 
> 
> 
> 
> 
> dev.off()
null device 
          1 
>