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A new blood test to detect leukemia is under development. The cutoff threshold for a positive test result is moved from point B to point C. This change will have which of following statistical effects?

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Imagine that a person gets the results of a colon cancer screening test.

There are two possible scenarios - either the result is positive, indicating that they have colon cancer, or the result is negative, indicating they don’t have colon cancer.

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.

Let’s say that we recruit 1000 people - 100 people with colon cancer and 900 people without colon cancer - and then we give them all the same screening test.

That way we can see how many people with positive results actually have colon cancer and how many people with negative results actually don’t have colon cancer.

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.

A true positive would be a person who gets a positive test result and has colon cancer.

A true negative would be a person who gets a negative test result and doesn’t have colon cancer.

A false positive would be a person who gets a positive test result even though they don’t have colon cancer.

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.

A test with a perfect positive predictive value would have 100 true positives in cell a, because the test would correctly identify everyone who has colon cancer, and zero false positives in cell b.

To calculate negative predictive value, we divide the number of true negatives by the total number of people who tested negative - so cell d divided by the sum of cell c and d.

A test with perfect specificity would have 900 true negatives in cell d, because the test would correctly identify everyone who doesn’t have colon cancer, and zero false negatives, in cell c.

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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