Estimated true prevalence and predictive values from survey testing

Input Values

Sample size :
Number positive :
Test sensitivity :
Test specificity :
Sample size for sensitivity estimation :
Sample size for specificity estimation :
Confidence level :

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 the Wilson binomial approximation 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.

If sample sizes for sensitivity and specificity estimation are provided, confidence limits are based on variance estimates incorporating additional uncertainty associated with these values, also as described by Rogan and Gladen (1978). Otherwise, confidence limits assume sensitivity and specificity are known exactly and use the method 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. See the author's home page or follow the link to Confidence limits for prevalence adjusted for sensitivity/specificity for more information.


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