This false positive rate calculator determines the rate of incorrectly identified tests with the false positive and true negative values. There are instructions on how the calculation works below the form.
How does this false positive rate calculator work?
This health tool uses prevalence and specificity to compute the false positive rate along with the false positive and true negative values.
There are two fields in the false positive rate calculator, each with a choice of % (between 0 and 100%), fraction or ratio (0 to 1) for the input of data.
In order for the fields to be defined, the following table needs to be introduced in the explanation:
| Test Result | Disease | Non Disease | Total Number |
| Positive | True Positive | False Positive | Total Test Positive |
| Negative | False Negative | True Negative | Total Test Negative |
| Total Disease | Total Non Disease | Total |
■ Prevalence – is calculated as total disease divided by total and multiplied by 100 and its value is influenced by the dimensions of the study group.
■ Specificity – (True Negative Rate) is defined as the fraction of subjects without the disease and whose test is negative. It quantifies the avoidance of false positive. Specificity can be extracted from the following: True Negative / (True Negative + False Positive) x 100.
The results provided in the above calculation are the following:
■ False Positive – defined as non disease incorrectly identified through test as disease.
■ True Negative – defined as non disease correctly identified as non disease.
■ False Positive Rate – rate of incorrectly identified out of total non disease.
Please note that as characteristics of the test, sensitivity and specificity are not influenced by the dimension of the studied population.
The formulas used are presented in the table below:
| Result | Formula |
| False Positive | (1 - Specificity) x (1 - Prevalence) |
| True Negative | Specificity x (1 - Prevalence) |
| False Positive Rate | 100 x False Positive / (False Positive + True Negative) |
References
1) Lalkhen AG, McCluskye A. (2008) Clinical tests: sensitivity and specificity. Contin Educ Anaesth Crit Care Pain; 8(6): 221-223.
2) Jakobsdottir J, Weeks DE. (2007) Estimating Prevalence, False-Positive Rate, and False-Negative Rate with Use of Repeated Testing When True Responses Are Unknown. Am J Hum Genet; 81(5): 1111–1113.
3) Suojanen JN (1999) False False Positive Rates. N Engl J Med; 341:131.
27 Aug, 2016