rsu.sep.rsfreecalc {epiR} | R Documentation |

Calculates the surveillance system (population-level) sensitivity for detection of disease assuming representative sampling and imperfect test sensitivity and specificity.

rsu.sep.rsfreecalc(N, n, c = 1, pstar, se.u, sp.u)

`N` |
scalar, integer representing the total number of subjects eligible to be sampled. Use |

`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 |

`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. |

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.

A scalar representing the surveillance system (population-level) sensitivity.

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.

## 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 2.0.31 Index]