Negative Predictive Value (NPV) 

When individuals at risk of a condition undergoes a diagnostic or screening test, the negative predictive value (NPV) is the proportion of those who test negative and indeed do not have the condition (true negatives). NPV is influenced by both the sensitivity and specificity of the test and the prevalence of the condition among those tested.

Key aspects of NPV include:

– True Negatives: The proportion of individuals who test negative and truly do not have the condition.

– Influencing Factors: NPV is affected by the test’s sensitivity (ability to correctly identify those with the condition) and specificity (ability to correctly identify those without the condition), as well as the prevalence of the condition in the tested population.

In economic modeling of diagnostic tests, NPV is important because 1-NPV indicates the proportion of individuals who are unlikely to receive further tests or interventions but could have potentially benefited (false negatives). This is particularly crucial in screening scenarios, where a false negative result might delay diagnosis and treatment, providing a false sense of reassurance.

NPV is closely related to positive predictive value (PPV), which measures the proportion of true positives among those who test positive. Both NPV and PPV are essential for evaluating the effectiveness and reliability of diagnostic tests, informing decisions about their use in clinical practice and health policy.