epiR: rsu.sspfree.rs – R documentation

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rsu.sspfree.rs

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.

epiR

Tools for the Analysis of Epidemiological Data

v2.0.19

GPL (>= 2)

Authors

Mark Stevenson <mark.stevenson1@unimelb.edu.au> and Evan Sergeant <evansergeant@gmail.com> with contributions from Telmo Nunes, Cord Heuer, Jonathon Marshall, Javier Sanchez, Ron Thornton, Jeno Reiczigel, Jim Robison-Cox, Paola Sebastiani, Peter Solymos, Kazuki Yoshida, Geoff Jones, Sarah Pirikahu, Simon Firestone, Ryan Kyle, Johann Popp, Mathew Jay and Charles Reynard.

Initial release

2021-01-12