Evaluation of Diagnostic Tests

Sensitivity

= True positive rate

Specificity

= True negative rate

⇒ False negative rate (1 - specificity)

There is always a trade-off between sensitivity and specificity of a diagnostic test.

Typically, as the sensitivity increases, the specificity decreases and vice-versa

Contingency tables (also known as 2 * 2 tables, see below) are used to illustrate and calculate test statistics such as sensitivity. It would be unusual for a medical exam not to feature a question based around screening test statistics. Commit the following table to memory and spend time practising using it as you will be expected to make calculations using it in your exam.

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 Plain english
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

Precision

The precision quantifies a tests ability to produce the same measurements with repeated tests.

Incidence and Prevalence

Incidence = the rate at which new diseases occur, measured in the number of new cases per unit time.

Prevalence = the total number of cases in a population