College Answer

Using the presence or absence of a disease, and the result a specific test as an example: Sensitivity = proportion of patients with disease detected by positive test = A/(A+C). Very high values essential if wish to catch all with disease, and allow a negative result to virtually rule out the diagnosis.

Specificity = proportion of patients without disease detected by negative test = D/(B+D). Very high values of specificity essential if wish to catch all without the disease, and allow a positive result to rule in the diagnosis.

Positive predictive value = proportion of patients with positive test who have disease = A/(A+B). PPV allows estimate of certainty around positive result.

Negative predictive value = proportion of patients with negative test who do not have disease = D/(C+D). NPV allows estimate of certainty about a negative result.

Discussion

Later papers focus merely on the candidate's ability to apply the formulae.

One can make a strong argument for a return to questions which test one's understanding of the actual concept, rather than demanding the regurgitation of rote-learned equations.

To rote-learn the abovemention equations, here is a helpful list.

Sensitivity = true positives / (true positives + false negatives)

This is the proportion of patients in whom disease which was correctly identified by the test.

Specificity = true negatives / (true negatives + false positives)

This is the proportion of patients in whom the disease was correctly excluded

Positive predictive value = (true positives / total positives)

This is the proportion of patients with positive test results who are correctly diagnosed.

Negative predictive value = (true negatives / total negatives)

This is the proportion of patients with negative test results who are correctly diagnosed.