Question

What is sensitivity, specificity, and predictive value?

Hint:

Sensitivity and specificity are used to evaluate the accuracy of a diagnostic test, while predictive values depend on both test accuracy and disease prevalence.

Answer 1

Step 1: Introduction to Diagnostic Test Evaluation.
Sensitivity, specificity, and predictive values are statistical measures used to evaluate the performance of diagnostic tests. These measures help determine the accuracy of a test in identifying true cases (sensitivity) and non-cases (specificity), as well as the test's predictive power.
Step 2: Sensitivity.
- Definition: Sensitivity refers to the ability of a test to correctly identify those with the disease (true positives). It is the proportion of actual positives correctly identified by the test.
- Formula: \[ \text{Sensitivity} = \frac{\text{True Positives}}{\text{True Positives} + \text{False Negatives}} \] - Interpretation: A test with high sensitivity is good at detecting the disease and will rarely miss true cases (low false negatives). It is crucial for screening tests where it is important not to miss any cases. Step 3: Specificity.
- Definition: Specificity refers to the ability of a test to correctly identify those without the disease (true negatives). It is the proportion of actual negatives correctly identified by the test.
- Formula: \[ \text{Specificity} = \frac{\text{True Negatives}}{\text{True Negatives} + \text{False Positives}} \] - Interpretation: A test with high specificity is good at ruling out those without the disease and will rarely result in false positives. It is important for confirmatory tests where false positives must be minimized.
Step 4: Predictive Values.
(1) Positive Predictive Value (PPV):
- Definition: PPV is the probability that a person who tests positive actually has the disease.
- Formula: \[ \text{PPV} = \frac{\text{True Positives}}{\text{True Positives} + \text{False Positives}} \] - Interpretation: PPV increases with higher disease prevalence and is more useful when considering whether a positive test result should lead to treatment.
(2) Negative Predictive Value (NPV):
- Definition: NPV is the probability that a person who tests negative actually does not have the disease.
- Formula: \[ \text{NPV} = \frac{\text{True Negatives}}{\text{True Negatives} + \text{False Negatives}} \] - Interpretation: NPV increases with lower disease prevalence and is helpful in ruling out the disease.
Step 5: Relationship Between Sensitivity, Specificity, and Predictive Values.
- Sensitivity and specificity are intrinsic properties of the test and remain constant regardless of disease prevalence.
- Predictive values, however, are influenced by disease prevalence in the population being tested. For example, as the prevalence of a disease increases, PPV increases, and NPV decreases.