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\name{rsu.pstar}
\alias{rsu.pstar}
\title{
Design prevalence back calculation
}
\description{
Calculates design prevalence required for given sample size and desired surveillance system (population-level) sensitivity, assuming representative sampling, imperfect test sensitivity and perfect test specificity.
}
\usage{
rsu.pstar(N = NA, n, se.p, se.u)
}
\arguments{
\item{N}{scalar or vector, integer representing the population size. Use \code{NA} if unknown.}
\item{n}{scalar or vector, integer representing the number of units sampled.}
\item{se.p}{scalar or vector of the same length as \code{n} representing the desired surveillance system (population-level) sensitivity.}
\item{se.u}{scalar or vector of the same length as \code{n} representing the unit sensitivity.}
}
\value{
A vector of design prevalence estimates.
}
\references{
MacDiarmid S (1988). Future options for brucellosis surveillance in New Zealand beef herds. New Zealand Veterinary Journal 36: 39 - 42.
Martin S, Shoukri M, Thorburn M (1992). Evaluating the health status of herds based on tests applied to individuals. Preventive Veterinary Medicine 14: 33 - 43.
}
\examples{
## EXAMPLE 1:
## In a study to provide evidence that your country is free of a given disease
## a total of 280 individuals are sampled. Assume a desired surveillance system
## sensitivity of 0.95 and an individual unit diagnostic sensitivity of 0.98.
## If all unit tests return a negative result, what is the maximum prevalence
## if disease is actually present in the population (i.e., what is the design
## prevalence)?
rsu.pstar(N = NA, n = 280, se.p = 0.95, se.u = 0.98)
## If 280 individuals are sampled and tested and each returns a negative test
## result we can be 95\% confident that the maximum prevalence (if disease is
## actually present in the population) is 0.011.
## EXAMPLE 2:
## In a study to provide evidence disease freedom a total of 30 individuals
## are sampled from a set of cattle herds. Assume cattle herds in the study
## region range from 100 to 5000 cows. As above, assume a desired surveillance
## system sensitivity of 0.95 and an individuals unit diagnostic sensitivity
## of 0.98. If all 30 unit tests return a negative result, what is the expected
## design prevalence for each herd?
round(rsu.pstar(N = c(100,500,1000,5000), n = 30,
se.p = 0.95, se.u = 0.98), digits = 3)
## The expected herd level design prevalence ranges from 0.086 (for a 100
## cow herd) to 0.102 (for a 5000 cow herd).
}
\keyword{methods}
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