Biostatistics in Epidemiology [Picmonic]

There’s a Picmonic for That!: Biostatistics in Epidemiology

Biostatistics is an often dreaded, underestimated topic when studying epidemiology. Evaluating, utilizing, and implementing the appropriate statistical equations takes time, practice, and thorough understanding. This skill will be important to master in order to ace preclinical exams, clinical rotations, and licensing exams. Luckily, there’s a Picmonic for that!

Sensitivity and specificity are statistical measures that indicate how well a test can measure the presence of a specific disease. 

Ideally, a perfect test or predictor would be 100% sensitive and 100% specific, meaning that the test would predict all people from the sick group as sick and exclude anyone from the healthy group as sick. However, for any test, there is usually a trade-off between these measures. 

Sensitivity relates to the test’s ability to identify positive results. It is the proportion of people known to have the disease and who test positive for it. The formula for identifying sensitivity equals the number of true positives divided by the (number of true positives + number of false negatives). A test with high sensitivity is often a reliable indicator of ruling out a disease because a negative result can reliably be assumed to be true. 

An easy way to remember this is remembering the word “SN OUT” meaning sensitivity is used to “rule OUT” disease. Because a test with high sensitivity rarely misses true positives among those who are actually positive, it is often used as a screening test to ensure that all people with the disease are identified. However, tests with high sensitivity can create false positives, meaning patients that do not have the disease may have a positive test result.