# epi.herdtest: Estimate the characteristics of diagnostic tests applied at... In epiR: Tools for the Analysis of Epidemiological Data

 epi.herdtest R Documentation

## Estimate the characteristics of diagnostic tests applied at the herd (group) level

### Description

When tests are applied to individuals within a group we may wish to designate the group as being either diseased or non-diseased on the basis of the individual test results. This function estimates sensitivity and specificity of this testing regime at the group (or herd) level.

### Usage

``````epi.herdtest(se, sp, P, N, n, k)
``````

### Arguments

 `se` a vector of length one defining the sensitivity of the individual test used. `sp` a vector of length one defining the specificity of the individual test used. `P` scalar, defining the estimated true prevalence. `N` scalar, defining the herd size. `n` scalar, defining the number of individuals to be tested per group (or herd). `k` scalar, defining the critical number of individuals testing positive that will denote the group as test positive.

### Value

A list with one scalar and two data frames.

Scalar `sfraction` reports the sampling fraction (i.e., `n / N`). The binomial distribution is recommended if `sfraction` is less than 0.2.

Data frame `dbinom` lists `APpos` the probability of obtaining a positive test, `APneg` the probability of obtaining a negative test, `HSe` the estimated group (herd) sensitivity, and `HSp` the estimated group (herd) specificity calculated using the binomial distribution.

Data frame `dhyper` lists `APpos` the probability of obtaining a positive test, `APneg` the probability of obtaining a negative test, `HSe` the estimated group (herd) sensitivity, and `HSp` the estimated group (herd) specificity calculated using the hypergeometric.

### Author(s)

Ron Thornton, MAF New Zealand, PO Box 2526 Wellington, New Zealand.

### References

Dohoo I, Martin W, Stryhn H (2003). Veterinary Epidemiologic Research. AVC Inc, Charlottetown, Prince Edward Island, Canada, pp. 113 - 115.

### Examples

``````## EXAMPLE 1:
## We want to estimate the herd-level sensitivity and specificity of
## a testing regime using an individual animal test of sensitivity 0.391
## and specificity 0.964. The estimated true prevalence of disease is 0.12.
## Assume that 60 individuals will be tested per herd and we have
## specified that two or more positive test results identify the herd
## as positive.

epi.herdtest(se = 0.391, sp = 0.964, P = 0.12, N = 1E06, n = 60, k = 2)

## This testing regime gives a herd sensitivity of 0.99 and a herd
## specificity of 0.36 (using the binomial distribution). With a herd
## sensitivity of 0.95 we can be confident that we will declare a herd
## as disease positive if it truly is disease positive. With a herd specficity
## of only 0.36, we will declare 0.64 of disease negative herds as infected,
## so false positives are a problem.
``````

epiR documentation built on June 22, 2024, 10:57 a.m.