Estimating prevalence
Sample size
Number positive
Test sensitivity
Test specificity
Confidence level
0.9
0.95
0.98
0.99
Confidence interval for apparent prevalence
Normal approximation
Clopper-Pearson exact
Wilson
Jeffreys
Agresti-Coull
Confidence interval for true prevalence
Normal approximation
Clopper-Pearson exact
Sterne
Blaker
Wilson
All
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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.

True prevalence estimates are calculated as described by:

Rogan and Gladen (1978). Estimating prevalence from the results of a screening test.

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.

Blaker's, Sterne, Clopper-Pearson and Wilson confidence limits are calculated as described by Reiczigel, Földi, Ózsvári (2010). Exact confidence limits for prevalence of a disease with an imperfect diagnostic test,

# Estimated true prevalence and predictive values from survey testing

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, 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.410 recent calculations