Common Registration Assessment, Part 1
Sensitivity, specificity and predictive value calculations
Three original questions on diagnostic test accuracy from a 2 by 2 table, with the working shown.
Published 14 October 2026. Independent revision material, not GPhC questions, not for patient care.
The NICE glossary defines sensitivity as the proportion of people classified as positive by the reference standard who are correctly identified by the study test, and specificity as the proportion classified as negative by the reference standard who are correctly identified. It defines positive predictive value as the proportion of people with a positive test result who actually have the disease, and negative predictive value as the proportion with a negative result who do not.
In a 2 by 2 table, call the true positives TP, false positives FP, false negatives FN and true negatives TN. Sensitivity is TP ÷ (TP + FN). Specificity is TN ÷ (TN + FP). Positive predictive value is TP ÷ (TP + FP). Negative predictive value is TN ÷ (TN + FN).
The method
- Fill in the 2 by 2 table: TP, FP, FN, TN.
- For sensitivity, divide TP by everyone who truly has the disease (TP + FN).
- For specificity, divide TN by everyone who truly does not have it (TN + FP).
- For predictive values, divide by everyone with the same test result: positives (TP + FP) or negatives (TN + FN).
- Convert to a percentage and round only at the end.
Three practice questions
Work each one on paper first, then open the answer. All patients and prescriptions are fictional.
Question 1
In 1000 people, a test gives 90 true positives, 30 false positives, 10 false negatives and 870 true negatives. What are the sensitivity, specificity, positive predictive value and negative predictive value?
Show the answer
Answer: Sensitivity 90%, specificity 96.7%, PPV 75%, NPV 98.9%
Working: Sensitivity: 90 ÷ (90 + 10) = 90 ÷ 100 = 90%. Specificity: 870 ÷ (870 + 30) = 870 ÷ 900 = 96.7%. PPV: 90 ÷ (90 + 30) = 90 ÷ 120 = 75%. NPV: 870 ÷ (870 + 10) = 870 ÷ 880 = 98.9%.
Question 2
A test has a sensitivity of 90% and a specificity of 95%. It is used in 1000 people where 2% have the disease. What is the positive predictive value?
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Answer: About 26.9%
Working: Diseased: 1000 × 2% = 20. True positives: 20 × 90% = 18. Not diseased: 980. False positives: 980 × 5% = 49. PPV: 18 ÷ (18 + 49) = 18 ÷ 67 = 26.9%.
Question 3
Of 200 people, 50 have the disease and the test finds 40 of them. Of the 150 without it, 15 test positive. What are the sensitivity, specificity and PPV?
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Answer: Sensitivity 80%, specificity 90%, PPV 72.7%
Working: TP = 40, FN = 10, FP = 15, TN = 150 - 15 = 135. Sensitivity: 40 ÷ 50 = 80%. Specificity: 135 ÷ 150 = 90%. PPV: 40 ÷ (40 + 15) = 40 ÷ 55 = 72.7%.
Exam approach
Draw the 2 by 2 table first, then choose the row or column total that matches the definition.
Where marks are lost
- Dividing by the total number of people instead of the right group.
- Swapping sensitivity and positive predictive value.
- Forgetting to work out the true negatives from the number without disease.
- Assuming that a high sensitivity and specificity always gives a high positive predictive value.
Frequently asked questions
What is sensitivity?
The NICE glossary defines it as the proportion of people classified as positive by the reference standard who are correctly identified by the study test.
What is positive predictive value?
The NICE glossary defines it as the proportion of people with a positive test result who actually have the disease or characteristic.
Sources
- NICE, guidelines manual appendix L glossary
- GPhC, Common Registration Assessment specification and permitted items for 2026
The questions above are original and use fictional patients and prescriptions. PreRegExamPrep is not affiliated with or endorsed by the General Pharmaceutical Council.
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