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Positive and negative predictive value
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Imagine that a person gets the results of a colon cancer screening test.
At this point, the person may ask themselves, how worried should I be that it was a positive test result? Or, how reassured should I be that it was a negative test result?
Each test has a positive predictive value, or PPV, which is the probability that people with a positive test result truly have the outcome, and a negative predictive value, or NPV, which is the probability that people with a negative test result truly don’t have the outcome.
Let’s take an example to show how it’s possible to measure a test’s predictive value.
We can organize the results using a 2 by 2 table, where the true disease status of the individual is on the top of the box, and the results of the screening test are on the side, and each of the cells is labeled a, b, c, or d.
And a false negative would be a person who gets a negative test result even though they have colon cancer.
To calculate the positive predictive value, we divide the number of true positives by the total number of people who tested positive - so cell a divided by the sum of cell a and b.
Positive predictive value (PPV) is a measure of the accuracy of a positive test result in a diagnostic test, calculated by dividing the number of true positives by the sum of true positives and false positives. It is used to quantify the likelihood that a person with a positive test result actually has the condition the test is designed to detect.
On the other hand, there is negative predictive value (NPV), which is a measure of the accuracy of a negative test result in a diagnostic test, calculated by dividing the number of true negatives by the sum of true negatives and false negatives. It is used to quantify the likelihood that a person with a negative test result does not have the condition the test is designed to detect.
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