Slide 1: In plain English…what exactly is sensitivity and specificity?
Slide 2: Sensitivity – The percent of diseased patients a test can capture. Specificity – The percent of healthy patients a test can exclude.
Slide 3: Sensitivity. The percent of diseased patients a test can capture. Also known as the “true positive rate.” Sensitivity = TP / (TP + FN). [diagram showing 25 diseased patients with 23 testing positive (true positive) and 2 testing negative (false negative)] in this sample: Sensitivity = 23/25 = 92%. False Negative Rate = 2/25 = 8%.
Slide 4: Specificity. The percent of healthy patients a test can exclude. Also known as the “true negative rate.” Specificity = TN / (TN + FP). [diagram showing 25 healthy patients with 1 testing positive (false positive) and 24 testing negative (true negative)] In this sample: Specificity = 24/25 = 96%. False Positive Rate = 1/25 = 4%.
Slide 5: Take note! It’s important to note that sensitivity and specificity are properties intrinsic to a test and do not take into account the underlying prevalence of disease.
Slide 6: Interpreting in Context. To determine the likelihood that your positive test is a true positive, you need to take into account the prevalence of disease in your population, and the pretest probability that your patient has the disease!
Tags: epidemiology, false negative rate, false positive rate, sensitivity, specificity, statistics