Surveillance system sensitivity for detection of disease assuming representative sampling and imperfect test sensitivity and specificity.
Calculates the surveillance system (population-level) sensitivity for detection of disease assuming representative sampling and imperfect test sensitivity and specificity.
rsu.sep.rsfreecalc(N, n, c = 1, pstar, se.u, sp.u)
N |
scalar, integer representing the total number of subjects eligible to be sampled. Use |
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 |
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. |
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.
A scalar representing the surveillance system (population-level) sensitivity.
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.
## 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.
Please choose more modern alternatives, such as Google Chrome or Mozilla Firefox.