epi.herdtest {epiR}R Documentation

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


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.


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



a vector of length one defining the sensitivity of the individual test used.


a vector of length one defining the specificity of the individual test used.


scalar, defining the estimated true prevalence.


scalar, defining the herd size.


scalar, defining the number of individuals to be tested per group (or herd).


scalar, defining the critical number of individuals testing positive that will denote the group as test positive.


A data frame with four elements: 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.


The method implemented in this function is based on the hypergeometric distribution.


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


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


## 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 0.95 and a herd
## specificity of 0.36. With a herd sensitivity of 0.95 we can be
## confident that we will declare a herd infected if it is infected.
## 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.

[Package epiR version 2.0.38 Index]