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Test characteristics: Difference between revisions
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Unfortunately, when designing a test, there is most commonly a tradeoff between sensitivity and specificity. One cannot design a test to be perfectly sensitive, as that would produce many false positives, as such giving a low specificity, and vice-versa. | Unfortunately, when designing a test, there is most commonly a tradeoff between sensitivity and specificity. One cannot design a test to be perfectly sensitive, as that would produce many false positives, as such giving a low specificity, and vice-versa. | ||
== Receive operating characteristic curve (ROC curve) == | |||
A receive operating characteristic curve (ROC curve) refers to a curve formed by a test's sensitivity on the y axis and the inverse of the specificity on the x axis. A ROC curve allows for visualisation of both sensitivity and specificity simultaneously. The area under the ROC curve is a good estimate of the test's performance. A perfect test has an area of under the ROC curve of 1, while the worst test has an area of 0.5. Most real-life tests lie somewhere in-between, but the closer to 1, the better. | |||
== Pre-test and post-test probability == | == Pre-test and post-test probability == | ||
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As with PPV, the negative predictive value of a test depends not only on the test's characteristics but also the pre-test probability of the disorder. However, in contrast to PPV, NPV ''decreases'' as the prevalence increases. As such, even if the test is really accurate and has a high specificity and sensitivity, the test may have a low negative predictive value regardless if the prevalence is high (the disease is common). | As with PPV, the negative predictive value of a test depends not only on the test's characteristics but also the pre-test probability of the disorder. However, in contrast to PPV, NPV ''decreases'' as the prevalence increases. As such, even if the test is really accurate and has a high specificity and sensitivity, the test may have a low negative predictive value regardless if the prevalence is high (the disease is common). | ||
== Precision and accuracy == | |||
The precision of a test or investigation refers to the reproducibility or consistency of the result. When repeating a test on the same sample, the machine should produce the same result every time. However, because machines and chemistry is impossible to accurately predict, there will be some variance in the results. When a test has a high precision, the results are very close to each other. Let's say that we have a blood glucose sample. and we use to separate methods to measure the blood glucose, one precise and one not, and we repeat the measurement five times for each. The results may look like this: | |||
* For the low-precision test: 6.5, 3.2, 4.9, 7.5, 3.1 | |||
* For the high-precision test: 4.5, 4.3, 4.6, 4.5, 4.4 | |||
It's obvious that the high-precision test is more valuable for us, but it's important to know that test accuracy is unrelated to precision. | |||
The accuracy of a test refers to how close the result is to the actual value in the sample, i.e. how well it represents the truth. For example, a test which measures the CRP as 9 mg/L when the CRP in the sample is actually 4 is not accurate. | |||
A test can be precise without being accurate. For example, in the aforementioned blood glucose example, if the blood glucose level in the sample was actually 4.5, the high-precision test was both precise and accurate. However, a high-precision low-accuracy test could produce the same results if the blood glucose level in the sample was 7.6, for example. | |||
== Analytical variation == | |||
When performing any test, there will be some random variation in the analysis. This can be because of temperature changes in the laboratory, differences in reagents, differences in pipetted volume of sample, etc. | |||
For example, when measuring leukocytes, there is a 2% analytical variation in the measurement when the true value is around 7x10<sup>9</sup>/L. As such, the result can vary by 0.14 units just due to analytical variation. A measurement of 7 one day and 6.9 the next day therefore does not necessarily reflect an actual decrease in the leukocytes in the sample; it could just be due to analytical variation. | |||
Analytical variation varies from laboratory to laboratory and from test to test. It can usually be looked up in your local laboratory handbook. | |||
Other examples of analytical variation (in my local laboratory): | |||
* <3% for values around 25 mg/L for CRP | |||
* 7% for values between 15 - 700 µg/L for ferritin | |||
* 9% for values around 20 ng/L for troponin I | |||
== Biological variation == | |||
The human body is tightly regulated by homeostasis, but no compound in the blood stays at the exact same level over time. The concentration of compounds in the blood change with age, time of day, food intake, | |||
== Reference range == | |||
When designing a quantitative test, one must determine a range of values which are regarded as "healthy". This is made by using the test to make many measurements on a large, healthy population and plotting those results. This forms a normal distribution curve. Then, the referance range is chosen so that 95% of the measurements from the large, healthy population end up within that range (each end of the range is two standard deviations from the mean). This means that 5% of healthy people have measurements that end up outside the reference range, which is important to know! As such, values which are slightly outside the reference range may be normal. However, the farther away from the reference range the value is, the higher the probability of pathology. | |||
The obvious next question is why we choose to define the reference range so that 5% of healthy people are outside the range. Why not define it so that 100% of the measurements from the healthy population end up in the range? The reason for this is threefold: | |||
# Because it affects the test's sensitivity | |||
#* A reference range defined by 99% instead of 95% of the healthy population would have a lower sensitivity (but a higher specificity, but we usually prefer a higher sensitivity) | |||
# It eliminates any outliers which would mess up the interval | |||
# Those with measurements in the 5% range may have subclinical disease | |||
[[Category:Public Health]] | [[Category:Public Health]] | ||
[[Category:Laboratory Medicine]] | [[Category:Laboratory Medicine]] | ||