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Negative Predictive Value (NPV)

## What is the difference between specificity and negative predictive value?

The significant difference is that PPV and NPV use the **prevalence of a condition to determine** the likelihood of a test diagnosing that specific disease. … Specificity is the percentage of true negatives (e.g. 90% specificity = 90% of people who do not have the target disease will test negative).

## What is the difference between positive predictive value and negative predictive value?

Positive predictive value is the probability that subjects with a positive screening test truly have the disease. Negative predictive value is the **probability that subjects with a negative screening test truly don’t have the disease**.

## How do you calculate positive predictive value from sensitivity and specificity?

For a mathematical explanation of this phenomenon, we can calculate the positive predictive value (PPV) as follows: **PPV = (sensitivity x prevalence) / [ (sensitivity x prevalence) + ((1 – specificity) x (1 – prevalence)) ]**

## What is considered a good positive predictive value?

In the general population, few diseases reach a prevalence of 1%. For example, if a test has **95% sensitivity and 95% specificity** (considered very good), then: … For a test with 99% sensitivity and 99% specificity, here are positive predictive values for different levels of prevalence.

## What is the negative predictive value?

Negative predictive value:

It is **the ratio of subjects truly diagnosed as negative to all those who had negative test results** (including patients who were incorrectly diagnosed as healthy). This characteristic can predict how likely it is for someone to truly be healthy, in case of a negative test result.

## What is a high negative predictive value?

The more sensitive a test, **the less likely an individual with a negative test will have the disease** and thus the greater the negative predictive value. The more specific the test, the less likely an individual with a positive test will be free from disease and the greater the positive predictive value.

## How do you interpret negative predictive value?

The negative predictive value is defined as the number of true negatives (people who test negative who don’t have a condition) **divided by the total number of people who test negative**.

## How do you read sensitivity and specificity results?

The sensitivity of the test reflects **the probability that the screening test will be positive among those who are diseased**. In contrast, the specificity of the test reflects the probability that the screening test will be negative among those who, in fact, do not have the disease.

## How do you explain sensitivity and specificity?

Sensitivity: **the ability of a test to correctly identify patients with a disease**. Specificity: the ability of a test to correctly identify people without the disease. True positive: the person has the disease and the test is positive. True negative: the person does not have the disease and the test is negative.

## What is considered high sensitivity?

In other words, a highly sensitive test is **one that correctly identifies patients with a disease**. A test that is 100% sensitive will identify all patients who have the disease. It’s extremely rare that any clinical test is 100% sensitive.

## What are true positives and false positives?

A **true positive is an outcome where the model correctly predicts the positive class**. Similarly, a true negative is an outcome where the model correctly predicts the negative class. A false positive is an outcome where the model incorrectly predicts the positive class.