rsu.sspfree.rs {epiR} R Documentation

## Sample size to achieve a desired probability of disease freedom assuming representative sampling

### Description

Calculates the required sample size to achieve a desired (posterior) probability of disease freedom assuming representative sampling, imperfect test sensitivity and perfect test specificity.

### Usage

```rsu.sspfree.rs(N = NA, prior, p.intro, pstar, pfree, se.u)
```

### Arguments

 `N` scalar integer or vector of integers the same length as `n`, representing the population size. Use `NA` if unknown. `prior` scalar probability (0 to 1), representing the prior probability that the population is free of disease. `p.intro` scalar or vector of the same length as `pfree`, representing the probability of disease introduction during the next time period. `pstar` scalar numeric or vector of numbers the same length as `pfree` representing the design prevalence. `pfree` scalar numeric or vector of numbers the same length as `pfree` representing the desired probability of disease freedom. `se.u` scalar (0 to 1) representing the sensitivity of the diagnostic test at the surveillance unit level.

### Value

A list comprised of three elements:

 `n` a vector listing the required sample sizes. `sep` a vector listing the population sensitivity estimates. `adj.prior` a vector listing the adjusted priors.

### Note

This function returns the sample size to achieve a desired (posterior) probability of disease freedom. Function `rsu.sssep.rs` returns the sample size to achieve a desired surveillance system sensitivity.

### References

Martin P, Cameron A, Greiner M (2007). Demonstrating freedom from disease using multiple complex data sources 1: A new methodology based on scenario trees. Preventive Veterinary Medicine 79: 71 - 97.

Martin P, Cameron A, Barfod K, Sergeant E, Greiner M (2007). Demonstrating freedom from disease using multiple complex data sources 2: Case study - Classical swine fever in Denmark. Preventive Veterinary Medicine 79: 98 - 115.

### Examples

```## EXAMPLE 1:
## Prior surveillance activities and expert opinion lead you to believe that
## there's a 75% chance that your country is free of disease X. To confirm
## your country's disease freedom status you intend to use a test at the herd
## level which has a diagnostic sensitivity of 0.95. The probability of
## disease introduction during the time period of interest is relatively
## low, say 0.01. How many herds need to be sampled to be 95% confident
## that the country is free of disease X assuming a design prevalence of
## 0.01?

rsu.sspfree.rs(N = NA, prior = 0.75, p.intro = 0.01, pstar = 0.01,
pfree = 0.95, se.u = 0.95)

## A total of 198 herds need to be sampled to meet the requirements of the
## study.

```

[Package epiR version 2.0.38 Index]