# Sensitivity, Specificity, PPV, NPV and Accuracy

Modified on Thu, 16 Mar 2023 at 09:29 PM

## Sensitivity and Specificity

Sensitivity and specificity are statistical measures used in medical research to evaluate the accuracy of a diagnostic test or screening tool.

Sensitivity refers to the ability of a test to correctly identify individuals who have a particular condition or disease. It is the proportion of people with the disease who test positive on the diagnostic test. A high sensitivity means that the test has a low rate of false negatives, meaning that there are few cases of the disease that go undetected.

Specificity, on the other hand, refers to the ability of a test to correctly identify individuals who do not have a particular condition or disease. It is the proportion of people without the disease who test negative on the diagnostic test. A high specificity means that the test has a low rate of false positives, meaning that there are few cases where the test indicates the presence of the disease when it is actually absent.

In general, a good diagnostic test should have both high sensitivity and high specificity. However, there is often a trade-off between sensitivity and specificity, and the optimal balance depends on the specific clinical context and the consequences of false positives and false negatives.

## Positive and Negative Predictive Values (PPV and NPV)

Positive predictive value (PPV) and negative predictive value (NPV) are also statistical measures used in medical research to evaluate the accuracy of a diagnostic test or screening tool.

PPV is the proportion of people who test positive for a condition and actually have the condition. It is calculated as the number of true positive results divided by the total number of positive results.

NPV is the proportion of people who test negative for a condition and do not have the condition. It is calculated as the number of true negative results divided by the total number of negative results.

PPV and NPV depend on the prevalence of the disease in the population being tested. When the prevalence of the disease is low, even highly specific and sensitive tests can produce a high number of false positives, resulting in a low PPV. Conversely, when the prevalence of the disease is high, even tests with lower sensitivity and specificity can produce a high PPV.

PPV and NPV are important measures in helping clinicians and researchers to interpret the results of diagnostic tests and to make decisions about the appropriate course of treatment or further testing.

## Should I prefer sensitivity and specificity or PPV and NPV to assess the accuracy of a test?

The choice between sensitivity and specificity versus PPV and NPV to assess the accuracy of a test depends on the clinical context and the purpose of the test.

Sensitivity and specificity are measures of the performance of a test, regardless of the prevalence of the disease in the population being tested. They are useful when the test is intended to rule in or rule out a specific diagnosis or condition, such as in screening programs or confirmation of disease presence.

PPV and NPV, on the other hand, are measures of the predictive value of the test in a specific population, which takes into account the prevalence of the disease in that population. They are useful when the test is intended to guide clinical decision-making, such as in the diagnosis of a specific condition or in the selection of treatment options.

Therefore, both measures can be useful in different contexts, and it's important to understand the clinical context and the purpose of the test when selecting the appropriate measure to assess the accuracy of the test.

Let's say we are evaluating a new diagnostic test for COVID-19 in a population with a prevalence of the disease of 10%.

If we are interested in the overall performance of the test, we might use sensitivity and specificity as our measures. Suppose the sensitivity of the test is 95% and the specificity is 90%. This means that the test correctly identifies 95% of people with COVID-19 (true positives) and correctly identifies 90% of people without COVID-19 (true negatives). However, there is a 5% false-negative rate, meaning that 5% of people with COVID-19 will be missed by the test, and a 10% false-positive rate, meaning that 10% of people without COVID-19 will be wrongly diagnosed as having the disease.

If we are interested in using the test to guide clinical decision-making, we might use PPV and NPV as our measures. In this case, the PPV is the proportion of people who test positive and actually have COVID-19. Suppose the PPV of the test is 60%, meaning that of all the people who test positive, 60% actually have COVID-19. The NPV, on the other hand, is the proportion of people who test negative and do not have COVID-19. Suppose the NPV is 99%, meaning that of all the people who test negative, 99% do not have COVID-19.

In this example, both sets of measures are useful, but for different purposes. Sensitivity and specificity are measures of the overall performance of the test, while PPV and NPV are measures of the predictive value of the test in a specific population. It's important to consider the clinical context and the purpose of the test when selecting the appropriate measure to assess its accuracy.

## What is accuracy?

Accuracy is a measure of how well a diagnostic test correctly identifies the presence or absence of a condition or disease. It is the proportion of all test results (positive and negative) that are correct, or the number of true positives and true negatives divided by the total number of tests performed.

Accuracy is an important measure of the overall performance of a diagnostic test, but it should be used with caution, especially when the prevalence of the disease is low. In such cases, even highly accurate tests can produce a high number of false positives, which can lead to unnecessary follow-up tests or treatments.

Therefore, while accuracy is an important measure, it is not always sufficient on its own to evaluate the performance of a diagnostic test. Other measures, such as sensitivity, specificity, positive predictive value, and negative predictive value, may also be needed to provide a more complete picture of the diagnostic accuracy of the test.