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\name{rsu.sep.rsfreecalc}
\alias{rsu.sep.rsfreecalc}
\title{
Surveillance system sensitivity for detection of disease assuming representative sampling and imperfect test sensitivity and specificity.
}
\description{
Calculates the surveillance system (population-level) sensitivity for detection of disease assuming representative sampling and imperfect test sensitivity and specificity.}
\usage{
rsu.sep.rsfreecalc(N, n, c = 1, pstar, se.u, sp.u)
}
\arguments{
\item{N}{scalar, integer representing the total number of subjects eligible to be sampled. Use \code{NA} if unknown.}
\item{n}{scalar, integer representing the total number of subjects sampled.}
\item{c}{scalar, integer representing the cut-point number of positives to classify a cluster as positive. If the number of positives is less than \code{c} the cluster is negative; if the number of positives is greater than or equal to \code{c} the cluster is positive.}
\item{pstar}{scalar, numeric, representing the design prevalence, the hypothetical outcome prevalence to be detected. See details, below.}
\item{se.u}{scalar, numeric (0 to 1) representing the diagnostic sensitivity of the test at the unit level.}
\item{sp.u}{scalar, numeric (0 to 1) representing the diagnostic specificity of the test at the unit level.}
}
\details{
If a value for \code{N} is entered surveillance system sensitivity is calculated using the hypergeometric distribution. If \code{N} is \code{NA} surveillance system sensitivity is calculated using the binomial distribution.
}
\value{
A scalar representing the surveillance system (population-level) sensitivity.}
\references{
Cameron A, Baldock C (1998a). A new probability formula for surveys to substantiate freedom from disease. Preventive Veterinary Medicine 34: 1 - 17.
Cameron A, Baldock C (1998b). Two-stage sampling in surveys to substantiate freedom from disease. Preventive Veterinary Medicine 34: 19 - 30.
Cameron A (1999). Survey Toolbox for Livestock Diseases --- A practical manual and software package for active surveillance of livestock diseases in developing countries. Australian Centre for International Agricultural Research, Canberra, Australia.
}
\examples{
## EXAMPLE 1:
## Thirty animals from a herd of 150 are to be tested using a test with
## diagnostic sensitivity 0.90 and specificity 0.98. What is the
## surveillance system sensitivity assuming a design prevalence of 0.10 and
## two or more positive tests will be interpreted as a positive result?
rsu.sep.rsfreecalc(N = 150, n = 30, c = 2, pstar = 0.10,
se.u = 0.90, sp.u = 0.98)
## If a random sample of 30 animals is taken from a population of 150 and
## a positive test result is defined as two or more individuals returning
## a positive test, the probability of detecting disease if the population is
## diseased at a prevalence of 0.10 is 0.87.
## EXAMPLE 2:
## Repeat these calculations assuming herd size is unknown:
rsu.sep.rsfreecalc(N = NA, n = 30, c = 2, pstar = 0.10,
se.u = 0.90, sp.u = 0.98)
## If a random sample of 30 animals is taken from a population of unknown size
## and a positive test result is defined as two or more individuals returning
## a positive test, the probability of detecting disease if the population is
## diseased at a prevalence of 0.10 is 0.85.
}
\keyword{univar}
|
