13th February 2024

Evaluation of diagnostic tests

Another study you could be assessed on involves determining the adequacy and appropriateness of a diagnostic or screening test. These can be assessed by examining the sensitivity, specificity, positive and negative predictive values and likelihood ratios.

The simplest way to understand how to determine these ratios is by using the following 2×2 table:

Adapted from: Shreffler J, Huecker MR (2021). Diagnostic Testing Accuracy: Sensitivity, Specificity, Predictive Values and Likelihood Ratios. In: StatPearls. United States: StatPearls Publishing. Available from: https://www.ncbi.nlm.nih.gov/books/NBK557491/ 

Sensitivity and specificity

Sensitivity defines the proportion of true positives in the population, i.e., it is the test’s ability to yield a positive result for someone who has the disease.

On the other hand, specificity defines the proportion of true negatives in the population, i.e., it is the test’s ability to yield a negative result for someone without the disease.

Both sensitivity and specificity are used together to determine the validity of a diagnostic test. They are inversely related such that as the sensitivity of a test increases, its specificity tends to decrease. A test with higher sensitivity is more beneficial to exclude a disease if a person tests negative. A highly specific test, however, is helpful to “rule in” a diagnosis when the person tests positive.

Positive and negative predictive values

In a clinical setting with the test result in front of you, the positive predictive value (PPV) and negative predictive value (NPV) help the clinician understand how to interpret a result and explain it to the patient in front of them.

prevalence of a disease also affects the PPV and NPV. The higher the disease prevalence, the greater the PPV; conversely, the NPV would decrease.

Positive and negative likelihood ratios

The literature increasingly quotes likelihood ratios as they can be clinically more relevant than sensitivity and specificity. They are also unaffected by disease prevalence, unlike predictive values, but they can comment on how much the utilisation of a test can alter its probability. Therefore, it can be used to calculate an individual’s disease probability. They are calculated as follows:

An LR+ is the probability of a person with the disease having a positive test result divided by the probability of a person without the disease having a positive test result. If the LR+ is >1, a positive result is more likely to occur in individuals with the disease than in those without. If the LR+ is between 0 and 1, then the converse is true: a positive result is less likely to occur in individuals with the disease than in those without.

An LR- is the probability of a person with the disease having a negative result divided by the probability of a person without the disease having a negative result. Therefore, an LR- >1 means a negative result is more likely to occur in those with the disease than in those without, and an LR- between 0 and 1 indicates a negative result is less likely to occur in those with the disease than those without.