Estimated true prevalence and predictive values from survey testing

Input Values

Sample size:
Number positive:
Test sensitivity:
Test specificity:
Confidence level:
Confidence interval for apparent prevalence:
Confidence interval for true prevalence:
   Estimate the true prevalence, as well as positive and negative predictive values and likelihood ratios from survey testing results using a test of known sensitivity and specificity.

Confidence limits for both apparent and true prevalence estimates are calculated. Values are also plotted for a range of possible survey results.

Confidence limits for apparent prevalence use methods from:
Brown, LD, Cat, TT and DasGupta, A (2001). Interval Estimation for a proportion. Statistical Science 16:101-133.

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.

True prevalence estimates that are less than zero or greater than one are not consistent with assumed sensitivity and specificity values, and are indicated by '<0' and '>1', respectively.

Confidence limit calculations assume sensitivity and specificity are known exactly. The normal approximation method uses the formula described by Greiner, M and Gardner, IA (2000). Application of diagnostic tests in epidemiologic studies. Preventive Veterinary Medicine 45:43-59.

Blaker's, Sterne, Clopper-Pearson and Wilson confidence limits are calculated as described by Reiczigel, Földi and Ózsvári (2010). Exact confidence limits for prevalence of a disease with an imperfect diagnostic test, Epidemiology and Infection 138:1674-1678. The authors recommend Blaker's interval for general use.


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