rsu.sep.rsfreecalc {epiR} R Documentation

## 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

 `N` scalar, integer representing the total number of subjects eligible to be sampled. Use `NA` if unknown. `n` scalar, integer representing the total number of subjects sampled. `c` scalar, integer representing the cut-point number of positives to classify a cluster as positive. If the number of positives is less than `c` the cluster is negative; if the number of positives is greater than or equal to `c` the cluster is positive. `pstar` scalar, numeric, representing the design prevalence, the hypothetical outcome prevalence to be detected. See details, below. `se.u` scalar, numeric (0 to 1) representing the diagnostic sensitivity of the test at the unit level. `sp.u` scalar, numeric (0 to 1) representing the diagnostic specificity of the test at the unit level.

### Details

If a value for `N` is entered surveillance system sensitivity is calculated using the hypergeometric distribution. If `N` is `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.

```

[Package epiR version 2.0.38 Index]