epiR (version 2.0.43)

rsu.spp.rs: Surveillance system specificity assuming representative sampling

Description

Calculates surveillance system (population level) specificity assuming representative sampling and imperfect test specificity.

Usage

rsu.spp.rs(N, n, c = 1, sp.u)

Arguments

N

scalar or vector of the same length as that vector of n defining the [cluster] population size. Use NA if the size of the population not known, or for a more general application see details, below.

n

scalar or vector defining the sample size.

c

scalar or vector of the same length as that vector of n defining the cut-point number of positives to classify a cluster as positive, if the number of positive samples is less than c the cluster is declared is negative, if the number of positive samples is greater than c the cluster is declared positive.

sp.u

scalar (0 to 1) or vector of same length as n, the specificity of the diagnostic test at the surveillance unit level.

Value

A vector of population specificity estimates.

Details

This function calculates population specificity using the hypergeometric distribution if N and c are provided and the binomial distribution otherwise.

If N is provided the number of false positives is fixed, based on N and test specificity sp.u. This implies that test specificity is a fixed individual-level characteristic (e.g., due to specific cross-reacting infection). If N is not supplied, cluster (e.g., herd) specificity is a random binomial function based only on the number of samples and test specificity (i.e., specificity is a function of the test and independent of individual characteristics).

References

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:
## Calculate the surveillance system specificity (i.e., the probability that 
## an uninfected population will be correctly identified as negative) if 30 
## surveillance units have been tested from a population of 150 using a 
## diagnostic test with surveillance unit specificity of 0.90, using a 
## cut-point of one or more positives to consider the population positive.

## A specificity of 0.90 means that 9 out of 10 samples from disease-negative
## surveillance units will return a negative result (i.e., one of them will be
## a false positive).
 
rsu.spp.rs(N = 150, n = 30, c = 1, sp.u = 0.90)

## The surveillance system specificity is 0.03. There is a probability of 
## 0.03 that all 30 samples will be negative.   


## EXAMPLE 2:
## Now assume we set a cut-point of 6. That is, 6 or more samples have to 
## return a positive result for us to declare the population positive:

rsu.spp.rs(N = 150, n = 30, c = 6, sp.u = 0.90)

## The surveillance system specificity is 0.95.

# }