#### II. Definitions

1. Test Sensitivity
1. Screening Test's ability to identify true disease
2. A Test with high sensitivity has few False Negatives
3. Independent of disease Prevalence in the community
4. Sensitive Tests allow user to RULE OUT a condition (mnemonic "SNout")
2. Recall
1. Identical to the Test Sensitivity, but Recall term is used more in data science
2. Reflects what percentage of relevant items are positive (not missed as False Negatives)
3. See Test Precision (Positive Predictive Value)

#### III. Calculation

1. Test Sensitivity
1. True positive tests per total affected patients tested
2. Test Sensitivity = P(positive test | disease)
1. Where P (A | B) = Probability of A given B
3. Expressed as a percentage
2. False Negative Rate
1. True cases missed by the Screening Test (test negative despite presence of condition)
2. False Negative Rate = 1 - Test Sensitivity

#### IV. Example: A new Screening Test for Crohn's Disease

1. Patients with known Crohn's Disease tested: 45
2. Patients with known Crohn's Disease who have a positive test: 36
3. Sensitivity = 36/45 or 80%