epiR (version 2.0.76)

rsu.sep.rsfreecalc: Surveillance system sensitivity for detection of disease assuming representative sampling and imperfect test sensitivity and specificity.

Description

Calculates the surveillance system (population-level) sensitivity for detection of disease assuming representative sampling and imperfect test sensitivity and specificity.

Usage

rsu.sep.rsfreecalc(N, n, c = 1, pstar, se.u, sp.u)

Value

A scalar representing the surveillance system (population-level) sensitivity.

Arguments

N

scalar, integer representing the total number of subjects eligible to be sampled. Use NA if unknown.

n

scalar, integer representing the total number of subjects sampled.

c

scalar, integer representing the cut-point number of positives to classify a cluster as positive. If the number of positives is less than c the cluster is negative; if the number of positives is greater than or equal to c the cluster is positive.

pstar

scalar, numeric, representing the design prevalence, the hypothetical outcome prevalence to be detected. See details, below.

se.u

scalar, numeric (0 to 1) representing the diagnostic sensitivity of the test at the unit level.

sp.u

scalar, numeric (0 to 1) representing the diagnostic specificity of the test at the unit level.

Details

If a value for N is entered surveillance system sensitivity is calculated using the hypergeometric distribution. If N is NA surveillance system sensitivity is calculated using the binomial distribution.

References

Cameron A, Baldock C (1998a). A new probability formula for surveys to substantiate freedom from disease. Preventive Veterinary Medicine 34: 1 - 17.

Cameron A, Baldock C (1998b). Two-stage sampling in surveys to substantiate freedom from disease. Preventive Veterinary Medicine 34: 19 - 30.

Cameron A (1999). Survey Toolbox for Livestock Diseases --- A practical manual and software package for active surveillance of livestock diseases in developing countries. Australian Centre for International Agricultural Research, Canberra, Australia.

Examples

## EXAMPLE 1:
## Thirty animals from a herd of 150 are to be tested using a test with
## diagnostic sensitivity 0.90 and specificity 0.98. What is the 
## surveillance system sensitivity assuming a design prevalence of 0.10 and 
## two or more positive tests will be interpreted as a positive result?

rsu.sep.rsfreecalc(N = 150, n = 30, c = 2, pstar = 0.10, 
   se.u = 0.90, sp.u = 0.98)

## If a random sample of 30 animals is taken from a population of 150 and 
## a positive test result is defined as two or more individuals returning 
## a positive test, the probability of detecting disease if the population is 
## diseased at a prevalence of 0.10 is 0.87.


## EXAMPLE 2:
## Repeat these calculations assuming herd size is unknown:

rsu.sep.rsfreecalc(N = NA, n = 30, c = 2, pstar = 0.10, 
   se.u = 0.90, sp.u = 0.98)

## If a random sample of 30 animals is taken from a population of unknown size 
## and a positive test result is defined as two or more individuals returning 
## a positive test, the probability of detecting disease if the population is 
## diseased at a prevalence of 0.10 is 0.85.