Outline the way you would calculate and how you might use the following features of a diagnostic test: sensitivity, specificity, positive predictive value and negative predictive value.
Disease
Present Absent
|
Test |
Positive |
A |
B |
A+B |
|
Negative |
C |
D |
C+D |
|
|
A+C |
B+D |
A+B + C+D |
Sensitivity = proportion of patients with disease detected by positive test = A/(A+C) Specificity = proportion of patients without disease detected by negative test = D/(B+D)
Positive predictive value = proportion of patients with positive test who have disease = A/(A+B) Negative predictive value = proportion of patients with negative test who do not have disease = D/(C+D)
Very high sensitivity means few false negatives. Very high specificity means few false positives.
This question closely resembles a whole mass of other questions:
The questions may not be identical, but they test the exact same concepts Here's a helpful list of equations the college expects us to memorise.
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.