B. Specificity – Explanation
Increasing the cut-off of a positive test result will decrease the number of false positives and hence
increase the specificity
Screening test statistics
It would be unusual for a medical exam not to feature a question based around screening test
statistics. The available data should be used to construct a contingency table as below:
TP = true positive; FP = false positive; TN = true negative; FN = false negative
| Disease present | Disease absent | |
| Test positive | TP | FP |
| Test negative | FN | TN |
The table below lists the main statistical terms used in relation to screening tests:
| Measure | Formula | Explanation |
| Sensitivity | TP / (TP + FN) | Proportion of patients with the condition who have a positive test result |
| Specificity | TN / (TN + FP) | Proportion of patients without the condition who have a negative test result |
| Positive predictive value | TP / (TP + FP) | The chance that the patient has the condition if the diagnostic test is positive |
| Negative predictive value | TN / (TN + FN) | The chance that the patient does not have the condition if the diagnostic test is negative |
| Likelihood ratio for a positive test result | sensitivity / (1 – specificity) | How much the odds of the disease increase when a test is positive |
| Likelihood ratio for a negative test result | (1 – sensitivity) / specificity | How much the odds of the disease decrease when a test is negative |
Positive and negative predictive values are prevalence dependent. Likelihood ratios are not
prevalence dependent
