What additional measurement is required to assess the predictive value of a negative test result (PV-)?

To assess the predictive value of a negative test result (PV-), it is important to incorporate data on false negatives. The predictive value of a negative test provides information on the likelihood that a negative result accurately reflects the absence of a condition or disease.

For a more accurate calculation of PV-, the formula used is:

PV- = True Negatives / (True Negatives + False Negatives)

This demonstrates that both true negatives and false negatives are essential for calculating the predictive value. False negatives represent instances in which the test incorrectly identifies a positive case as negative, leading to a potential underestimation of PV-. The presence of false negatives thus directly affects how confident we can be in a negative result indicating that the patient does not have the disease in question.

While true positives and true negatives are important for assessing the overall performance of a test, they do not influence PV- as directly as false negatives do. The true negatives, while necessary for the calculation, do not alone provide insight into the accuracy of negative results without considering how many cases were misclassified as negative when they were actually positive.