How to Calculate Negative and Positive Predictive Value

What Are Predictive Values?

Predictive values are statistical measures that quantify the accuracy of a diagnostic test. They help determine how reliable a test result is in predicting the presence or absence of a specific condition.

There are two main types of predictive values:

• Positive Predictive Value (PPV): The probability that a person has the condition given a positive test result.
• Negative Predictive Value (NPV): The probability that a person does not have the condition given a negative test result.

These values are crucial in medical decision-making, helping clinicians assess the likelihood of a condition based on test results.

Positive Predictive Value (PPV)

Positive Predictive Value (PPV) answers the question: "If a test is positive, how likely is it that the person actually has the condition?"

Where:

• True Positives (TP): Number of people correctly identified with the condition.
• False Positives (FP): Number of people incorrectly identified with the condition.

A high PPV means the test is good at identifying people who truly have the condition, minimizing false alarms. However, a high PPV doesn't necessarily mean the test is good at identifying people who don't have the condition.

Negative Predictive Value (NPV)

Negative Predictive Value (NPV) answers the question: "If a test is negative, how likely is it that the person does not have the condition?"

Where:

• True Negatives (TN): Number of people correctly identified as not having the condition.
• False Negatives (FN): Number of people incorrectly identified as not having the condition.

A high NPV means the test is good at identifying people who truly don't have the condition, minimizing missed cases. However, a high NPV doesn't necessarily mean the test is good at identifying people who do have the condition.

How to Calculate Predictive Values

To calculate PPV and NPV, you need the following data from a diagnostic test:

• Number of true positives (TP)
• Number of false positives (FP)
• Number of true negatives (TN)
• Number of false negatives (FN)

These values are typically presented in a 2×2 contingency table:

Using the calculator on this page, you can input these values to compute PPV and NPV.

Example Calculation

Let's consider a hypothetical example where a diagnostic test is evaluated:

• True Positives (TP): 90
• False Positives (FP): 10
• True Negatives (TN): 80
• False Negatives (FN): 20

Using the formulas:

In this example, the test has a 90% chance of correctly identifying people with the condition when the test is positive, and an 80% chance of correctly identifying people without the condition when the test is negative.

Interpreting Results

Interpreting predictive values requires understanding their limitations:

• PPV is high when false positives are low relative to true positives.
• NPV is high when false negatives are low relative to true negatives.
• Both values depend on the prevalence of the condition in the population being tested.
• A test with high PPV may have low NPV and vice versa, depending on the condition's prevalence.

Clinicians should consider both predictive values along with other factors like test sensitivity and specificity when evaluating diagnostic accuracy.