All 10 Questions With Complete Rationales
Prefer to read straight through, or want to review after taking the quiz? Every question, the correct answer, and the reasoning behind it are laid out below.
Question 1
A clinical trial shows that 20% of patients on Drug A experienced an MI compared to 30% on placebo. What is the Number Needed to Treat (NNT)?
A. 5 B. 10 C. 15 D. 20
✅ Answer: B — 10
NNT = 10. ARR (Absolute Risk Reduction) = 30% − 20% = 10% = 0.10. NNT = 1 / ARR = 1 / 0.10 = 10. This means you must treat 10 patients to prevent 1 MI. Always remember: NNT = 1/ARR, not 1/RRR.
Question 2
A new drug reduces the risk of stroke from 8% to 5%. The drug causes GI bleeding in 2% of users (vs 0.5% in placebo). What is the NNH for GI bleeding?
A. 50 B. 67 C. 100 D. 200
✅ Answer: B — 67
NNH = 67. ARI (Absolute Risk Increase) for GI bleeding = 2% − 0.5% = 1.5% = 0.015. NNH = 1 / 0.015 = 66.7 ≈ 67. For benefit-harm comparison: NNT for stroke prevention = 1/0.03 ≈ 33. NNH 67 means for every 67 treated, 1 gets GI bleeding.
Question 3
A screening test for diabetes has sensitivity of 95% and specificity of 60%. A patient tests positive. Which statement is most accurate?
A. The patient has a 95% chance of having diabetes B. A negative result would reliably rule out diabetes C. The test generates many false positives due to low specificity D. Specificity of 60% makes this test too unreliable to use
✅ Answer: C — The test generates many false positives due to low specificity
Low specificity = many false positives. Specificity of 60% means 40% of disease-free patients will test positive (false positives). High sensitivity (95%) makes this test good at ruling OUT disease — a negative result reliably means no diabetes (SnNout). But a positive result must be confirmed because of the high false-positive rate.
Question 4
A test for H. pylori has sensitivity 90%, specificity 85%. In a population where 10% have H. pylori, a patient tests positive. What is the approximate positive predictive value (PPV)?
A. 40% B. 60% C. 85% D. 90%
✅ Answer: A — 40%
PPV ≈ 40%. In low-prevalence populations, PPV drops significantly. Using Bayes: TP = 0.90 × 100 = 90; FP = 0.15 × 900 = 135. PPV = 90 / (90+135) = 90/225 ≈ 40%. This is a key NAPLEX concept: even a 'good' test has poor PPV in low-prevalence settings. This is why population screening requires confirmation testing.
Question 5
A cohort study follows 1,000 smokers and 1,000 non-smokers for 20 years. 200 smokers and 80 non-smokers develop lung cancer. What is the Relative Risk (RR)?
A. 1.5 B. 2.5 C. 3.0 D. 4.0
✅ Answer: B — 2.5
RR = 2.5. Risk in smokers = 200/1000 = 0.20 (20%). Risk in non-smokers = 80/1000 = 0.08 (8%). RR = 0.20 / 0.08 = 2.5. Smokers are 2.5 times more likely to develop lung cancer. RR > 1 = increased risk; RR < 1 = protective; RR = 1 = no association.
Question 6
Which study design is best suited to evaluate the causality between a rare exposure and a common disease outcome?
A. Case-control study B. Randomized controlled trial C. Cohort study D. Cross-sectional study
✅ Answer: C — Cohort study
Cohort study. Cohort studies are ideal for common outcomes with known exposures because they follow exposed vs unexposed groups forward in time and calculate Relative Risk directly. Case-control studies are better for rare diseases (not rare exposures). RCTs are the gold standard for causality but are impractical for rare exposures due to ethical/feasibility issues.
Question 7
A study reports an odds ratio (OR) of 3.2 with a 95% confidence interval of 1.1–9.4. What can you conclude?
A. The result is not statistically significant B. There is a statistically significant association but with wide uncertainty C. The exposure reduces risk by 3.2 times D. The 95% CI confirms no clinical significance
✅ Answer: B — There is a statistically significant association but with wide uncertainty
Statistically significant but wide CI. The CI (1.1–9.4) does NOT cross 1.0, so the result is statistically significant (p < 0.05). However, the wide confidence interval suggests high variability — the true OR could be anywhere from 1.1 to 9.4. OR > 1 means increased odds (not decreased). Statistical significance does not equal clinical significance.
Question 8
A drug reduces the Relative Risk of MI by 25% (RRR = 25%). In the control group, the event rate is 4%. What is the Absolute Risk Reduction (ARR)?
A. 0.25% B. 1% C. 4% D. 25%
✅ Answer: B — 1%
ARR = 1%. ARR = RRR × Control Event Rate = 0.25 × 0.04 = 0.01 = 1%. This illustrates why RRR can be misleading. A '25% reduction' sounds impressive, but the ARR of 1% means NNT = 100. Always look at ARR and NNT — not just RRR — when evaluating clinical benefit.
Question 9
Which type of study would generate the highest level of evidence for a clinical practice guideline?
A. Well-designed case-control study B. Prospective cohort study C. Systematic review of RCTs with meta-analysis D. Expert consensus opinion
✅ Answer: C — Systematic review of RCTs with meta-analysis
Systematic review with meta-analysis. The evidence hierarchy (from highest to lowest): 1) Systematic reviews/meta-analyses of RCTs → 2) Individual RCTs → 3) Cohort studies → 4) Case-control studies → 5) Case series → 6) Expert opinion. Meta-analyses pool data from multiple RCTs, increasing statistical power and generalizability.
Question 10
A pharmacist evaluates a study on a new antihypertensive. The p-value is 0.03 and the absolute risk reduction is 0.5%. What should the pharmacist conclude?
A. The drug is both statistically and clinically significant B. The drug is statistically significant but may lack clinical significance C. The drug is not statistically significant D. A p-value of 0.03 confirms the drug should be first-line
✅ Answer: B — The drug is statistically significant but may lack clinical significance
Statistically significant, clinically questionable. p = 0.03 < 0.05, so the result is statistically significant. However, ARR of 0.5% means NNT = 200 — you must treat 200 patients to prevent 1 event. This is likely not clinically meaningful, especially if the drug is expensive or has side effects. Always integrate both statistical AND clinical significance into pharmacy decisions.
📌 How to use these
Answer each question before reading the rationale, and treat "right but unsure" as wrong. The rationale matters more than the answer — if you cannot explain why the other three options fail, you have not learned the rule yet. Ready for more? Work through the other free quizzes or the pharmacy law cheat sheet.