Diagnostic test are invaluable tools in our medical arsenal, but they are not without pitfalls. These tests can be used to identify diseases in their early states or eliminate competing diagnoses to find the correct treatment course. However, no test is perfect. When a health person receives a positive result, this can result in anxiety, additional (often invasive) tests, and occasionally misdiagnosis/over-treatment.
Let’s look at an example:
The numbers for this example come from and open-access article cited below.
- 50% of people have the condition that we’re screening for (“Prevalence” or Π)
- 90% of people with the disease will be correctly identified as having the condition (“Sensitivity” or S)
- 30% of people without the disease will be correctly identified as being healthy (“Specificity” or Sp)
If we give the screening test to 10,000 people, we would expect the following results:
|Positive Test||True Positive|
|Negative Test||False Negative|
For a given diagnostic test, there are many related parameters that we might care about. Some of these terms include prevalence, sensitivity, false negative rate, specificity, false positive rate, accuracy, likelihood ratio,and much more. I’m a visual person and wanted a way to represent these parameters and their relationships visually.
The visualization below demonstrates the performance of diagnostic tests with relevant parameters marked along the outside of the square.
- The square represent all people that receive our diagnostic test
- The left column represent the people with the condition.
- The right column represent healthy people.
- The green regions represent correct test results.
I’m working on an interactive version of this chart, so stay tuned. My hope is that being able to play with each parameter will result in a deeper intuition for how these parameters relate.
Maxim LD, Niebo R, Utell MJ. Screening tests: a review with examples. Inhal Toxicol. 2014;26(13):811–828. doi:10.3109/08958378.2014.955932