epiR (version 2.0.17)

rsu.sssep.rs: Sample size to achieve a desired surveillance system sensitivity assuming representative sampling

Description

Calculates the sample size to achieve a desired surveillance system sensitivity assuming representative sampling for a single risk factor and varying unit sensitivity using the binomial method.

Usage

rsu.sssep.rs(N, pstar, se.p = 0.95, se.u)

Arguments

N

scalar integer or vector of same length as pstar, representing the population size.

pstar

scalar (0 to 1) or vector of same length as pstar, design prevalence.

se.p

scalar or vector of same length as pstar representing the desired surveillance system (population-level) sensitivity.

se.u

scalar (0 to 1) or vector of the same length as pstar representing the sensitivity of the diagnostic test at the surveillance unit level.

Value

A vector of required sample sizes.

References

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

# NOT RUN {
## EXAMPLE 1:
## You would like to confirm the absence of disease in a single 1000-cow 
## dairy herd. You expect the prevalence of disease in the herd to be 0.05.
## You intend to use a single test with a sensitivity of 0.90 and a 
## specificity of 1.00. How many herds need to be sampled if you want to 
## be 95% certain that the prevalence of brucellosis in dairy herds is 
## less than the design prevalence if all tests are negative?

rsu.sssep.rs(N = 1000, pstar = 0.05, se.p = 0.95, se.u = 0.90)

## We need to sample 65 cows.


## EXAMPLE 2:
## You would like to confirm the absence of disease in a study area comprised 
## of 5000 herds. If the disease is present you expect the between-herd 
## prevalence to be 0.08. You intend to use two tests: the first has a 
## sensitivity and specificity of 0.90 and 0.80, respectively. The second has 
## a sensitivity and specificity of 0.95 and 0.85, respectively. The two tests 
## will be interpreted in parallel. How many herds should be sampled to be 
## 95% certain that the disease would be detected if it is present in the 
## study area?

## Calculate the sensitivity and specificity of the diagnostic test regime:

test <- rsu.dxtest(se = c(0.90, 0.95), sp = c(0.80, 0.85), 
   interpretation = "parallel", covar = c(0,0))
   
## Interpretation of these tests in parallel returns a diagnostic sensitivity
## of 0.995 and a diagnostic specificity of 0.68.

## How many herds should be sampled?

rsu.sssep.rs(N = 5000, pstar = 0.08, se.p = 0.95, se.u = test$se)

## If you test 38 herds and all return a negative test you can state that 
## you are 95% confident that the disease is absent from the study area.
## The sensitivity of this testing regime is 99%.


## EXAMPLE 3:
## You want to document the absence of Mycoplasma from a 200-sow pig herd.
## Based on your experience and the literature, a minimum of 20% of sows  
## would have seroconverted if Mycoplasma were present in the herd. How 
## many herds should we sample to be 95% certain that Mycoplasma would 
## be detected if it is present if you use a test with perfect sensitivity? 

rsu.sssep.rs(N = 200, pstar = 0.20, se.p = 0.95, se.u = 1.00)

## If you test 15 sows and all of them test negative you can be 95% 
## confident that the prevalence rate of Mycoplasma in the herd is less than
## 20%.
# }