rsu.sspfree.rs: Sample size to achieve a desired probability of disease...

In epiR: Tools for the Analysis of Epidemiological Data

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 documentation built on Sept. 30, 2024, 9:16 a.m.