Cluster-level sensitivity and specificity with variable cut-points

Input Values


This utility estimates the probability of detecting disease (herd or cluster-sensitivity) in a large (infinite) population, if it is present at the specified design prevalence, assuming a test of known sensitivity and specificity and for a variable cut-point number fo positives to determine the test result. These analyses use the method from Martin et el. (1992) (Prev Vet Med, 14:33-43) and the binomial distribution function, assuming known test sensitivity and test specificity and a variable cut-point number of positives to declare a population infected (i.e. a variable (non-zero) number of positive positives can be allowed and still be recognised as free). The population is classified as infected if the number of positive results is equal to or greater than the cut-point.

Inputs are the sample size tested, test sensitivity, test specificity, the design (target) prevalence and the cut-point number of positives.

Outputs are:

  • the cluster-level sensitivity (SeH) and specificity (SpH) for the given sample size, test sensitivity, test specificity, design prevalence and cut-point number of positives; and
  • tables and graphs of cluster-level sensitivity and specificity values for a range of cut-point, sample size and design prevalence values.

Design (target) prevalence:
Unit Sensitivity:
Unit Specificity:
Sample Size:
Cut-point number of positives:


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