These methods use frequentist approaches to estimate prevalence and confidence limits,
assuming a fixed pool size and perfect (100%) test sensitivity and specificity,
as described below. See demonstration analysis
Method 1
This method assumes 100% test sensitivity and specificity and fixed pool size.
Asymptotic confidence limits are based on a normal approximation and may be <0 for low
prevalence values. See the User Guide,
Cowling et al. (1999) (Method 2) or
Sacks et al. (1989) for more details.
Method 2
This method assumes 100% test sensitivity and specificity and fixed pool size.
Exact confidence limits are calculated based on binomial theory, so that confidence limits
cannot be <0 or >1. See the User Guide or
Cowling et al. (1999) (Method 3) for more details.
Input values
Required inputs for these methods are pool size, number of pools tested, number of pools
positive and desired upper and lower confidence limits for the estimate. Pool size, number of
pools and number of pools positive must be positive integers and the number of positive pools
must be less than the number of pools tested. Upper and lower confidence limits must be between
zero and one
Outputs
Outputs for these methods are a point estimate, upper and lower confidence limits
(asymptotic or exact) and the standard error for the estimated prevalence as specified. A graph and
text file listing of estimates and confidence limits for all possible results are also
created for downloading if desired, by clicking on the appropriate icon.
Please Note:
For both methods, the algorithms used to calculate estimates and confidence limits fail if
either all or none of the pools are positive. In these cases the estimates are 100% and 0%
respectively.
