rsu.sssep.rs2st {epiR} R Documentation

## Sample size to achieve a desired surveillance system sensitivity assuming two-stage sampling

### Description

Calculates the required sample size to achieve a desired surveillance system sensitivity assuming two-stage sampling (sampling of clusters and sampling of units within clusters), imperfect test sensitivity and perfect test specificity.

### Usage

```rsu.sssep.rs2st(H = NA, N = NA, pstar.c, se.c, pstar.u, se.u, se.p)
```

### Arguments

 `H` scalar, integer representing the total number of clusters in the population. Use `NA` if unknown. `N` vector, integer representing the number of units within each cluster. Use `NA` if unknown. `pstar.c` scalar, numeric (0 to 1) representing the cluster level design prevalence. `se.c` scalar, numeric (0 to 1) representing the required cluster level sensitivity. `pstar.u` scalar, numeric (0 to 1) representing the surveillance unit level design prevalence. `se.u` scalar (0 to 1) representing the sensitivity of the diagnostic test at the surveillance unit level. `se.p` scalar (0 to 1) representing the desired surveillance system (population-level) sensitivity.

### Value

A list comprised of two data frames: `clusters` and `units`. Data frame `clusters` lists:

 `H` the total number of clusters in the population, as entered by the user. `nsample` the number of clusters to be sampled.

Data frame `units` lists:

 `N` the number of units within each cluster, as entered by the user. `nsample` the number of units to be sampled.

### References

Cameron A, Baldock C (1998). A new probability formula for surveys to substantiate freedom from disease. Preventive Veterinary Medicine 34: 1 - 17.

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.

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:
## Sampling is to be carried out to support a claim that a country is free
## of bovine brucellosis. We are not certain of the total number of herds
## in the country and we are not certain of the number of cows within each
## herd.

## The design prevalence for this study is set to 0.01 at the herd level and
## if a herd is positive for brucellosis the individual animal level
## design prevalence is set to 0.10. The sensitivity of the diagnostic
## test to be used is 0.95.

## How many herds and how many animals from within each herd
## need to be sampled to be 95% confident of detecting disease at the
## herd and individual animal level?

rsu.sssep.rs2st(H = NA, N = NA, pstar.c = 0.01, se.c = 0.95,
pstar.u = 0.10, se.u = 0.95, se.p = 0.95)

## A total of 314 herds need to be sampled, 31 cows from each herd.

## EXAMPLE 2:
## Now lets say we know that there are 500 cattle herds in the country and
## we have the results of a recent livestock census providing counts of the
## number of cattle in each herd. How many herds and how many animals from
## within each herd need to be sampled to be 95% confident of detecting
## disease at the herd and individual animal level?

# Generate a vector of herd sizes. The minimum herd size is 25.

set.seed(1234)
hsize <- ceiling(rlnorm(n = 500, meanlog = 1.5, sdlog = 2)) + 25

nsample <- rsu.sssep.rs2st(H = 500, N = hsize, pstar.c = 0.01, se.c = 0.95,
pstar.u = 0.10, se.u = 0.95, se.p = 0.95)

nsample\$clusters