Voorspellingswaarde

Voorspellingswaarde, also known as predictive value, is a concept used in statistics and diagnostics to assess the probability of a condition being present or absent given a specific test result. It is distinct from sensitivity and specificity, which measure the accuracy of a test in identifying true positives and true negatives, respectively. There are two main types of predictive value: positive predictive value (PPV) and negative predictive value (NPV).

Positive predictive value refers to the probability that a subject truly has the condition of interest, given that the test result is positive. In other words, if a test comes back positive, what is the chance the person actually has the disease or condition? A high PPV indicates that a positive test result is a strong indicator of the presence of the condition.

Negative predictive value refers to the probability that a subject truly does not have the condition of interest, given that the test result is negative. If a test comes back negative, what is the chance the person actually does not have the disease or condition? A high NPV suggests that a negative test result is a reliable indicator of the absence of the condition.

Both PPV and NPV are influenced by the prevalence of the condition in the population being tested. In populations with low prevalence, even a highly sensitive and specific test can have a lower PPV, meaning a positive result might be a false positive. Conversely, in low prevalence settings, NPV tends to be higher. Understanding predictive values is crucial for interpreting diagnostic test results accurately and making informed clinical decisions.