epiR: rsu.sep.rspool – R documentation

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rsu.sep.rspool

Surveillance system sensitivity assuming representative sampling, imperfect pooled sensitivity and perfect pooled specificity

Description

Calculates the surveillance system (population-level) sensitivity and specificity for detection of disease assuming representative sampling and allowing for imperfect sensitivity and specificity of the pooled test.

Usage

rsu.sep.rspool(r, k, pstar, pse, psp = 1)

Arguments

`r`	scalar or vector representing the number of pools.
`k`	scalar or vector of the same length as `r` representing the number of individual units that contribute to each pool (i.e. the pool size).
`pstar`	scalar or vector of the same length as `r` representing the design prevalence.
`pse`	scalar or vector of the same length as `r` representing the pool-level sensitivity.
`psp`	scalar or vector of the same length as `r` representing the pool-level specificity.

Value

A list comprised of two elements:

`se.p`	scalar or vector, the surveillance system (population-level) sensitivity estimates.
`sp.p`	scalar or vector, the surveillance system (population-level) specificity estimates.

References

Christensen J, Gardner I (2000). Herd-level interpretation of test results for epidemiologic studies of animal diseases. Preventive Veterinary Medicine 45: 83 - 106.

Examples

## EXAMPLE 1:
## To confirm your country's disease freedom status you intend to use a test 
## applied at the herd level. The test is expensive so you decide to pool the 
## samples taken from individual herds. If you decide to collect 60 pools, 
## each comprised of samples from five herds what is the sensitivity of 
## disease detection assuming a design prevalence of 0.01 and the sensitivity
## and specificity of the pooled test equals 1.0? 

rsu.sep.rspool(r = 60, k = 5, pstar = 0.01, pse = 1, psp = 1)

## This testing regime returns a population-level sensitivity of disease 
## detection of 0.95.


## EXAMPLE 2:
## Repeat these calculations assuming the sensitivity of the pooled test    
## equals 0.90. 

rsu.sep.rspool(r = 60, k = 5, pstar = 0.01, pse = 0.90, psp = 1)

## If the sensitivity of the pooled test equals 0.90 the population-level 
## sensitivity of disease detection is 0.93. How can we improve population-
## level sensitivity? Answer: include more pools in the study.

rsu.sep.rspool(r = 70, k = 5, pstar = 0.01, pse = 0.90, psp = 1)

## Testing 70 pools, each comprised of samples from 5 herds returns a 
## population-level sensitivity of disease detection of 0.95.

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