Sensitivity, Specificity, & Positive & Negative

Instructions & Worksheet for Exercise #2 –
Sensitivity, Specificity, & Positive & Negative
Predictive Values of Screening Tests
Exercise #2 –PUBH 560
Evaluating Sensitivity & Specificity of Two Screening Tests:

First, download the Excel worksheet (“Exercise2.xlsx”), which displays made-up data from a study to determine the sensitivity and specificity of two serology (antibody) tests for the “Swim Flu”, a novel respiratory infection. There are twenty subjects, ten of whom had a confirmed diagnosis of “Swim Flu”, and ten of whom were confirmed disease-free.

Their serum antibody levels, as determined by Test A and Test B, are shown in the tables on the spreadsheet. High antibody level indicates a positive test and Low antibody level indicates a negative test. – . entering various cutpoints for the two antibody assays in the yellow-highlighted cell, you will see side-by-side tabulations of the numbers of true positives (TP), false negatives (FN), false positives (FP), and true negatives (TN). – . default, “9” has been entered as the antibody cutpoint. Calculate the sensitivity and specificity for Test A and Test B using the numbers from the table that is highlighted in pink on the Excel worksheet. Then enter “8”, “10”, “11”, and “12” and re-calculate the sensitivities and specificities for each of these cutpoints, entering the results in the table highlighted in green on the Excel worksheet. Finally, upload your responses to Blackboard Assessment Brief under “EX2”

QUESTIONS
1. Which of the two screening tests would you use to screen for “swim flu” and why?

2. What antibody cutpoint would be a reasonable cutpoint for Test B assuming that you would prefer equal rates of false positives and false negatives?

3. Calculate and interpret the positive predictive value (PPV) for Test A at antibody cutpoint of 10?

4. Calculate and interpret the positive predictive value (PPV) for Test B at antibody cutpoint of 10?
5. Discuss the implications of a 100% sensitive test. [Hint: Consider false positive or false negative rate].

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To determine which screening test to use to screen for “swim flu”, it is important to consider both sensitivity and specificity. Sensitivity measures the proportion of true positive cases (people with the disease) that are correctly identified by the test, while specificity measures the proportion of true negative cases (people without the disease) that are correctly identified by the test. A test with high sensitivity and specificity is desirable for screening purposes.

A reasonable cutpoint for Test B can be found by determining the point where the false positive rate and the false negative rate are equal. This can be done by calculating the sensitivities and specificities at different cutpoints and finding the cutpoint where the two are closest.

Positive predictive value (PPV) is the proportion of positive test results that are true positives. To calculate the PPV for Test A at a cutpoint of 10, you would divide the number of true positives (people with the disease who test positive) by the total number of positive test results. Interpretation of the PPV for Test A at cutpoint 10 would involve considering the trade-off between missing cases of the disease (lower sensitivity) and incorrect diagnoses (higher false positive rate).

Similar to question 3, the PPV for Test B at a cutpoint of 10 would be calculated by dividing the number of true positives by the total number of positive test results. The interpretation of the PPV for Test B at cutpoint 10 would also involve considering the trade-off between missing cases of the disease and incorrect diagnoses.

A 100% sensitive test means that all true positive cases (people with the disease) are correctly identified by the test. However, such a test may result in a high false positive rate, meaning that many people without the disease are incorrectly diagnosed. The implications of a high false positive rate can include additional testing and unnecessary treatment, which can lead to harm and decreased healthcare resource utilization.