rsu.spp.rs {epiR} R Documentation

## 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.

### 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).

### Value

A vector of population specificity estimates.

### 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

```## 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.

```

[Package epiR version 2.0.31 Index]