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\name{epi.fpos}
\alias{epi.fpos}
\title{
Calculate the expected number of false positive and false negative test results
}
\description{
Compute the number of true positives, false positives, true negatives and false negatives given the number of individuals tested, the design prevalence and diagnostic test sensitivity and specificity.
}
\usage{
epi.fpos(n, pstar, se.u, sp.u, conf.level = 0.95)
}
\arguments{
\item{n}{scalar, integer, the number of surveillance units tested.}
\item{pstar}{scalar, the expected design prevalence.}
\item{se.u}{scalar, the surveillance unit sensitivity.}
\item{sp.u}{scalar, the surveillance unit specificity.}
\item{conf.level}{magnitude of the returned confidence interval. Must be a single number between 0 and 1.}
}
\value{
A list containing the following:
\item{test.pos}{the expected median number of test positives and the uncertainty in the number of test positives, as specified by \code{conf.level}.}
\item{true.pos}{the expected median number of true positives and the uncertainty in the number of true positives, as specified by \code{conf.level}.}
\item{false.pos}{the expected median number of false positives units and the uncertainty in the number of false positives, as specified by \code{conf.level}.}
\item{test.neg}{the expected median number of test negatives and the uncertainty in the number of test negative, as specified by \code{conf.level}.}
\item{true.neg}{the expected median number of true negatives and the uncertainty in the number of true negative, as specified by \code{conf.level}.}
\item{false.neg}{the expected median number of false negatives and the uncertainty in the number of false negative, as specified by \code{conf.level}.}
}
\examples{
## EXAMPLE 1:
## A bulk milk tank test has been developed to detect bovine tuberculosis
## (bTB) in dairy herds. The diagnostic sensitivity and specificity of the
## test is 0.714 and 0.981, respectively.
## If there are 9,000 dairy herds in the population of interest and of that
## group 15 are thought to be bTB positive how many false positive test
## results can be expected if all herds are tested on a single occasion?
epi.fpos(n = 9000, pstar = 15 / 9000, se.u = 0.714, sp.u = 0.981,
conf.level = 0.95)$false.pos
## If all 9,000 herds are tested on a single occasion we can expect 171
## (95\% CI 146 to 197) false positive.
## How many false negatives are expected using this test regime?
epi.fpos(n = 9000, pstar = 15 / 9000, se.u = 0.714, sp.u = 0.981,
conf.level = 0.95)$false.neg
## If all 9,000 herds are tested on a single occasion we can expect 4
## (95\% CI 1 to 9) false negatives.
}
\keyword{univar}
|
