Biostats Review

One thing students always fear, be it on the USMLE, COMLEX, or Shelf exams, is biostatistics.

As a tutor, I have found that most students and medical schools are not spending nearly enough time teaching their students key statistical concepts and helping them learn to apply biostats when interpreting research.

But fear not: As we go through the new school year, I will be posting detailed biostatistics breakdowns with the hope of better preparing anyone reading this blog. Let’s get started.

Sensitivity

The sensitivity of a test refers to its’ ability to correctly identify patients with a specific disease. Tests with high sensitivity (i.e. ELISA for HIV) are important for conditions where early diagnosis is imperative for successful treatment. A test with high sensitivity will have a low number of false negatives.

When calculating sensitivity (or specificity), it is always important to start by constructing your 2×2 table.

	Positive Disease	Negative Disease
Positive Test Result	True Positive	False Positive
Negative Test Result	False Negative	True Negative

From here, you must commit the following formula to memory:

Sensitivity = True Positives/ (True Positives + False Negatives)

Now, let’s try a real example:

A new blood test has been created that will help diagnosis ovarian cancer. A study of 200 patients is performed; 100 with ovarian cancer and 100 without. Using this new test, 90 patients test positive for ovarian cancer with further workup showing that 80 of these patients with positive tests truly had ovarian cancer. What is the sensitivity of this new test?

A) 25%
B) 50%
C) 75%
D) 80%
E) 90%