We want accurate tests, don’t we? By that, don’t we mean that we want precise test results? Well, not exactly. Before we leave the subject of laboratory statistics, there are a few more words we need to learn.
Accuracy is the ability of a test to aim for the target. Visually, accuracy looks like this:
Even though none of the shots hits the bullseye, we can tell where the shooter is aiming.
Precision is the ability of a test to get the same answer repeatedly, illustrated like this:
Even though none of the shots hits the bullseye, all of the shots go to the same spot.
Putting these terms together, we can describe different aspects test performance. Poor accuracy and poor precision look like this:
The shots are not centered on the bullseye, and they do not hit the same spot. Together, favorable accuracy and precision can be illustrated like this:
All the shots hit the bullseye, repeatedly. This is what an accurate and precise test looks like, too. Accuracy and precision are both desirable but different aspects of test systems.
But are accuracy, precision, sensitivity and specificity enough to interpret tests? Consider the case where these aspects of a test system are optimized. In other words, the test is highly accurate, precise, sensitive and specific. But what if the condition being tested for is not prevalent in the tested population. Prevalence is the percentage of people in a given population who have the condition of a positive test. Consider a test is 99.99% specific. That means that for every 10,000 results, there is only 1 false positive. But let’s say that the condition exists in the population at a rate of only 1 per 10,000 individuals. In other words, out of 10,000 results, there is only one true positive result. But we’ve already said that out of 10,000 tests, there will be one false positive result. Therefore, if we get a positive result, there a 50% chance it’s the false positive, not the true positive. That is what is meant by the term positive predictive value.
Both positive and negative predictive values can be measured. These are expressed mathematically as PPV = TP / (TP + FP) and NPV = TN / (TN + FN), respectively. It’s okay to skip the math; just remember that predictive values are dependent on the prevalence of the condition; sensitivity and specificity are not.
Predictive value is important for test interpretation but should not be used for test selection. Instead, sensitivity, specificity, accuracy and precision should be used to guide appropriate test selection. These are the terms by which test systems are judged, and the key aspects of test performance analyzed by the FDA before tests are approved for use in the United States.
