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.
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.
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.
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 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.
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.