rsu.sssep.rs {epiR} | R Documentation |

Calculates the sample size to achieve a desired surveillance system sensitivity assuming representative sampling for a single risk factor and varying unit sensitivity using the binomial method.

rsu.sssep.rs(N, pstar, se.p = 0.95, se.u)

`N` |
scalar integer or vector of same length as |

`pstar` |
a scalar or vector of either proportions (0 to 1) or a positive integers representing the design prevalence. If |

`se.p` |
scalar or vector of same length as |

`se.u` |
scalar (0 to 1) or vector of the same length as |

A vector of required sample sizes.

This function calculates the required sample size using the hypergeometric distribution if `N`

is provided and the binomial distribution otherwise.

This function returns the sample size to achieve a desired surveillance system sensitivity. Function `rsu.sspfree.rs`

returns the sample size to achieve a desired (posterior) probability of disease freedom.

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.

## EXAMPLE 1: ## You would like to confirm the absence of disease in a single 1000-cow ## dairy herd. You expect the prevalence of disease in the herd to be 0.05. ## You intend to use a single test with a sensitivity of 0.90 and a ## specificity of 1.00. How many herds need to be sampled if you want to ## be 95% certain that the prevalence of brucellosis in dairy herds is ## less than the design prevalence if all tests are negative? rsu.sssep.rs(N = 1000, pstar = 0.05, se.p = 0.95, se.u = 0.90) ## We need to sample 65 cows. ## EXAMPLE 2: ## You would like to confirm the absence of disease in a study area comprised ## of 5000 herds. If the disease is present you expect the between-herd ## prevalence to be 0.08. You intend to use two tests: the first has a ## sensitivity and specificity of 0.90 and 0.80, respectively. The second has ## a sensitivity and specificity of 0.95 and 0.85, respectively. The two tests ## will be interpreted in parallel. How many herds should be sampled to be ## 95% certain that the disease would be detected if it is present in the ## study area? ## Calculate the sensitivity and specificity of the diagnostic test regime: test <- rsu.dxtest(se = c(0.90, 0.95), sp = c(0.80, 0.85), interpretation = "parallel", covar = c(0,0)) ## Interpretation of these tests in parallel returns a diagnostic sensitivity ## of 0.995 and a diagnostic specificity of 0.68. ## How many herds should be sampled? rsu.sssep.rs(N = 5000, pstar = 0.08, se.p = 0.95, se.u = test$se) ## If you test 38 herds and all return a negative test you can state that ## you are 95% confident that the disease is absent from the study area. ## The sensitivity of this testing regime is 99%. ## EXAMPLE 3: ## You want to document the absence of Mycoplasma from a 200-sow pig herd. ## Based on your experience and the literature, a minimum of 20% of sows ## would have seroconverted if Mycoplasma were present in the herd. How ## many herds should we sample to be 95% certain that Mycoplasma would ## be detected if it is present if you use a test with perfect sensitivity? rsu.sssep.rs(N = 200, pstar = 0.20, se.p = 0.95, se.u = 1.00) ## If you test 15 sows and all of them test negative you can be 95% ## confident that the prevalence rate of Mycoplasma in the herd is less than ## 20%.

[Package *epiR* version 2.0.31 Index]