This false negative rate calculator determines the rate of incorrectly identified tests, the false negative and true positive values. Below the form there is information on the formulas on which the calculation is based.

## How does the false negative rate calculator work?

This health tool uses prevalence and sensitivity to determine the false negative rate along with the false negative, true positive and pre test odds.

There are two fields, each with a choice of % (0 to 100%), fraction or ratio (between 0 and 1) for the input of data.

■ Prevalence of disease is calculated as total disease divided by total and multiplied by 100. Prevalence is influenced by the dimension of the population in the study.

■ Sensitivity (True Positive Rate) is defined as the probability that a test will indicate disease amongst the subject with the disease. It quantifies the avoidance of false negatives. Sensitivity can also be extracted from the following: True Positive / (True Positive + False Negative) x 100

The calculation above provides the following results:

■ False Negative – defined as disease subjects incorrectly identified as non disease.

■ True Positive – defined as disease subjects correctly identified as disease.

■ Pre Test Odds – also known as Pre Test probability, is the subjective probability of the presence of a condition (Disease), before the diagnostic test.

■ False Negative Rate – rate of incorrectly identified as non disease out of total disease.

The above definitions follow the rules explained in the table below:

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 |

Sensitivity and specificity are characteristics of the test and are not influenced by the population in the study.

The formulas used in the false negative rate calculator are the following:

Result |
Formula |

False Negative | (1 - Sensitivity) x Prevalence |

True Positive | Sensitivity x Prevalence |

Pre Test Odds | Prevalence / (1 - Prevalence) |

False Negative Rate | 100 x False Negative / (True Positive + False Negative) |

## References

1) Lalkhen AG, McCluskye A. (2008) Clinical tests: sensitivity and specificity. Contin Educ Anaesth Crit Care Pain; 8(6): 221-223.

2) Griner PF, Mayewski RJ, Mushlin AI, Greenland P. (1981) Selection and interpretation of diagnostic tests and procedures. Principles and applications. Ann Intern Med; 94(4 Pt 2):557-92.

27 Aug, 2016