These methods use frequentist approaches to estimate prevalence and confidence limits, assuming a fixed pool size and a test with known values for sensitivity and specificity, as described below. See demonstration analysis.
This method assumes a fixed pool size and that test sensitivity and specificity are known exactly. Asymptotic confidence limits are based on a normal approximation and may be <0 for low prevalence values. See the User Guide or Messam et al. (2008), Equation (15) for more details.
This method assumes a fixed pool size and that test sensitivity and specificity are known exactly. Exact confidence limits are calculated based on binomial theory, so that confidence limits are never <0 or >1. See the User Guide or Cowling et al. (1999) (Method 5) for more details.
Required inputs for these methods are pool size, number of pools tested, number of pools positive, test sensitivity and specificity 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. Sensitivity, specificity and 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.
Estimates are only valid if the proportion of positive pools is greater than the false positive rate (1 - Specificity) and less than or equal to the true positive rate (Sensitivity). Invalid results are indicated by NA in the results table.