rsu.pstar {epiR} R Documentation

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

 `N` scalar or vector, integer representing the population size. Use `NA` if unknown. `n` scalar or vector, integer representing the number of units sampled. `se.p` scalar or vector of the same length as `n` representing the desired surveillance system (population-level) sensitivity. `se.u` scalar or vector of the same length as `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 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).
```

[Package epiR version 2.0.31 Index]