# Data Analysis

Gold Standard

PositiveNegativeTotal
SussStatPositive9040130
Negative10860870
1009001000

You are now ready to compute some statistics that will tell you how well SussStat performs compared to the gold standard of clinical diagnosis. Sensitivity and specificity are commonly used measures of the validity of a screening of a test (Aschengrau & Seage, pp. 421-422). Validity is the ability of a test to correctly categorize persons into their true disease status.

The measures of positive predictive value (PPV) and negative predictive value (NPV) describe how well a positive screening test result predicts presence or absence of a disease in a particular population. The PPV and NPV are measures of a screening program's feasibility (Aschengrau & Seage, p. 423).

2. Calculate the sensitivity, specificity, PPV, and NPV.

 a. Sensitivity = - 0.90 0.96 0.69 0.04 Answer — 0.90 b. Specificity = - 0.90 0.96 0.69 0.04 Answer — 0.96 c. PPV = - 0.90 0.96 0.69 0.04 Answer — 0.69 d. NPV = - 0.90 0.988 0.69 0.04 Answer — 0.988

3. Which is the best interpretation of the sensitivity of SussStat?

Answer (a) — incorrect: This is an interpretation of specificity.
Answer (b) — incorrect: This is the PPV of the test, PPV = TP/(TP+FP). The denominator of sensitivity is not those who tested positive, but rather those who truly have the disease.
Answer (c) — correct: Sensitivity = TP/(TP+FN).

4. Which is the best interpretation of the specificity of SussStat?

Answer (a) — incorrect: It is the probability that SussStat correctly categorized an individual as having Susser Syndrome, or the probability of obtaining a true negative.
Answer (b) — correct: Specificity= TN / (TN + FP).
Answer (c) — incorrect: Specificity= TN / TN + FP.

5. How could you interpret the positive predictive value (PPV)?

Answer (a) — incorrect: The is the interpretation of sensitivity.
Answer (b) — incorrect: This is the NPV
Answer (c) — correct: PPV = TP/(TP+FP).

You are concerned that there are too many false positives when the manufacturer-suggested cutpoint of DNA adducts is used to define the pre-clinical SS and you want to see if you can reduce the number by changing the cutpoint, or criterion of positivity, of the SussStat test. Click here for an interactive exercise demonstrating how raising or lowering the cutpoint changes the measures you calculated for SussStat.

6. What is a consequence if SussStat has a low sensitivity?

Answer (a) — correct: A low sensitivity means a lot of false negatives.
Answer (b) — incorrect: This is the consequence of a low specificity.
Answer (c) — incorrect: Only one of the answers listed is correct.

You are happy with the screening test's ability to identify Susser Syndrome, but you are now considering to what groups in Epiville you should target your screening program.

7. Using the sensitivity and specificity measures you calculated in Question 2 above, calculate what the PPV would be if you screen the total population of Epiville where you estimate the prevalence of SS to be only 1%. (hint: draw a new 2x2 table and write the new column totals for the Clinician Gold Standard using the new "true" population prevalence of 1%, given a total N=1000 Then use the specificity and sensitivity proportions you calculated previously to fill in the rest of the 2x2 table.) NOTE: ONLY USE THE SENSITIVITY AND SPECIFICITY VALUES ROUNDED TO 2 DECIMAL PLACES THAT APPEAR IN THE ANSWER TO QUESTION 2.

You make note of the fact that as the prevalence of the disease decreases, the PPV of the screening test decreases. You recommend to the EDOH that a screening program be introduced among workers of the Glop Industries since they have a higher prevalence of the disease, and therefore SussStat will be most effective in that group because it will detect a larger proportion of actual cases among individuals with positive results (Aschengrau & Seage, p. 424).

The measures you calculated above describe the validity of the SussStat test. In contrast, reliability is the ability of a test to give the same result on repeated testing, i.e., consistency. (Aschengrau & Seage, p. 419) Reliability can also be computed to describe the extent to which two tests agree with each other. A common measure of reliability is the kappa statistic.