It is important in diagnostic accuracy studies that the true clinical state of the patient is known. For example, in developing a SARS-CoV-2 anti-body test, for the positive subgroup, you might enlist subjects who had a positive SARS-CoV-2 PCR test and clinically confirmed illness. Then, for the negative subgroup, you might use samples taken from subjects before the illness was in circulation. It is also essential to consider other factors, such as the severity of illness, as they can have a marked effect on the performance characteristics of the test. A test that shows high sensitivity/specificity in a hospital situation in very ill patients can be much less effective in population screening where the severity of the illness is less.
In cases where the true condition of the subject is not known, and only results from a comparative method and a new test method are available, an agreement measure is more suitable. We will cover that scenario in detail in a future blog post.
In Analyse-it, there are two ways to arrange your data for this analysis.
Frequency form data summarizes the frequency counts for each combination of true state, test result:
Case form data lists the individual true state and test result for each subject:
You can find examples of both, and follow along with the steps below, using the workbook COVID-19 Diagnostic Accuracy Example.xlsx
The analysis report shows the sensitivity/specificity and other statistics.
To compute the predictive values given the prevalence of illness in a population:
If you have any questions about using the Analyse-it Method Validation edition for diagnostic accuracy studies, please contact us.
For more information, see our online documentation:Measures of diagnostic accuracy
Estimating the sensitivity and specificity of a binary test
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