MRCS PART A · APPLIED BASIC SCIENCES
Diagnostic-test denominators and verification
Four original five-option questions: two foundation calculations and two applied interpretations. Fictional study populations. FDA statistical guidance source checked. Educational statistical content only; publication, source verification and clinical review are separate statuses.
2 topics · 4 original SBAs
1 · Start with the reference-standard groups
Your task: Distinguish complete denominators from partial verification and identify what the observed data establish.
What belongs in the sensitivity denominator?
All people with the target condition by the reference standard: true positives plus false negatives. Sensitivity = TP/(TP+FN). A positive-test subgroup is a different denominator.
What belongs in the specificity denominator?
All people without the condition: true negatives plus false positives. Specificity = TN/(TN+FP), not TN divided by all tested people.
What does a false positive mean here?
The index test is positive but the reference standard classifies the condition as absent. A false negative has the opposite pairing. Keep the two classifications separate.
Is agreement with any other test automatically diagnostic accuracy?
No. Identify an appropriate reference standard; two tests can agree while both misclassify the condition.
If the positivity threshold changes, can old percentages be retained?
Recalculate from the new counts. Moving a threshold can change both true-positive and false-positive counts; the questions supply those observed outcomes rather than assuming a universal trade-off magnitude.
Does one point estimate establish performance in every clinic?
No. Study design, uncertainty and the tested population matter. A numerical exercise is not external validation.
If only positive results are verified, which denominator is complete?
When every positive result receives independent reference-standard classification, the positive-result group supports its observed PPV. The unverified negative group cannot simply be counted as condition-negative. Sensitivity, specificity, NPV and overall accuracy need additional condition classifications or a justified study design and analysis.
Does every partial-verification design make estimation impossible?
No. Designed sampling with appropriate adjustment can estimate performance. This question explicitly supplies neither verification of negatives nor an external model; naive complete-table calculations are unsupported. FDA discusses partial verification separately from complete-table formulas.
2 · Start with the positive-test group
Your task: Calculate positive predictive value and interpret a changed population under explicit model assumptions.
What belongs in the positive predictive value denominator?
All positive index-test results: true positives plus false positives. PPV = TP/(TP+FP), the condition-positive fraction of that result group.
Can 90% sensitivity be read as a 90% chance of disease after a positive result?
No. Those percentages condition on different groups. Count the positive results before interpreting the result group.
Why can population frequency change the positive-result group?
Under fixed sensitivity/specificity, fewer condition-positive people supply fewer true positives, while a larger condition-negative group can supply more false positives. Rebuild both counts before calculating PPV.
Are sensitivity and specificity guaranteed unchanged between populations?
No. The transfer is a stipulated model. Real changes in case mix, spectrum or conduct may change performance and need evidence.
What does a negative result leave unresolved?
Some negative results can be false negatives. NPV uses TN/(TN+FN), whereas the condition-positive fraction among negatives uses FN/(TN+FN). Neither equals a universal instruction to discharge.
What study information is needed before applying a percentage?
Define the target condition, index-test threshold, intended population and independent reference standard. NICE's methods framework assesses accuracy in that context, not a percentage alone.