| Search | |
|---|---|
Estimating prevalence
Estimate the true prevalence of infection from survey testing results, using a test with imperfect sensitivity and specificity.
Sensitivity and specificity estimates are specified as Beta probability distributions, with parameters alpha and beta. Beta probability distributions are commonly used to express uncertainty about a proportion based on a random sample of individuals. In this situation, if there are x "successes" out of n individuals examined, then the alpha and beta parameters can be calculated as alpha = x + 1 and beta = n - x + 1. Alternatively, alpha and beta can be calculated using the Beta distribution utility, provided estimates of the mode and 5% or 95% confidence limits are available from expert opinion or other data.
Simulated true prevalence estimates are calculated as described by:
Rogan and Gladen (1978). Estimating prevalence from the results of a screening test. American Journal of Epidemiology 107:71-76.
Outputs include a table and graphs summarising the probability distributions for true and apparent prevalence and test sensitivity and specificity.
Simulated true prevalence estimates from survey testing with an imperfect test
Estimate the true prevalence of infection from survey testing results, using a test with imperfect sensitivity and specificity.
Sensitivity and specificity estimates are specified as Beta probability distributions, with parameters alpha and beta. Beta probability distributions are commonly used to express uncertainty about a proportion based on a random sample of individuals. In this situation, if there are x "successes" out of n individuals examined, then the alpha and beta parameters can be calculated as alpha = x + 1 and beta = n - x + 1. Alternatively, alpha and beta can be calculated using the Beta distribution utility, provided estimates of the mode and 5% or 95% confidence limits are available from expert opinion or other data.
Simulated true prevalence estimates are calculated as described by:
Rogan and Gladen (1978). Estimating prevalence from the results of a screening test. American Journal of Epidemiology 107:71-76.
Outputs include a table and graphs summarising the probability distributions for true and apparent prevalence and test sensitivity and specificity.