EDAIC Statistics and Evidence-Based Medicine

Question 1

A screening test has a sensitivity of 90% and a specificity of 85%. In a population where the disease prevalence is 2%, what is the approximate positive predictive value (PPV)?

Accepted Answer

11%

Answer

PPV = (sensitivity × prevalence) / [(sensitivity × prevalence) + (1 − specificity) × (1 − prevalence)] = (0.90 × 0.02) / [(0.90 × 0.02) + (0.15 × 0.98)] = 0.018 / (0.018 + 0.147) ≈ 11%. At low disease prevalence, even a highly sensitive and specific test yields a low PPV, meaning most positive results are false positives.

Question 2

A randomised controlled trial reports 30-day mortality of 10% in the treatment group and 20% in the control group. What is the Number Needed to Treat (NNT)?

Accepted Answer

10

Answer

NNT = 1 / Absolute Risk Reduction (ARR). ARR = 20% − 10% = 10% = 0.10. NNT = 1 / 0.10 = 10. This means 10 patients must be treated to prevent one additional death. NNT is more clinically meaningful than relative risk reduction alone.

Question 3

A study reports a 95% confidence interval for the mean difference in arterial pressure between two groups as −2 to +8 mmHg. What is the most appropriate interpretation?

Accepted Answer

The result is not statistically significant at p < 0.05 because the interval includes zero

Answer

A 95% CI that crosses zero (the null value for a difference) means we cannot exclude no effect, which corresponds to p > 0.05 — the result is not statistically significant. Note this does not prove the treatment has no effect; the study may simply be underpowered. Clinical significance (is a difference of 8 mmHg important?) is a separate question from statistical significance.

Question 4

Which study design provides the highest level of evidence for the efficacy of a therapeutic intervention?

Accepted Answer

Systematic review with meta-analysis of multiple RCTs

Answer

The evidence hierarchy places systematic reviews and meta-analyses of high-quality RCTs at the top, because they pool data from multiple randomised trials, maximising statistical power and reducing random error. A single RCT ranks below a well-conducted systematic review; cohort and case-control studies are observational and more prone to confounding.

Question 5

In a diagnostic accuracy study, the area under the ROC curve (AUC) is reported as 0.50. How should this be interpreted?

Accepted Answer

The test performs no better than chance (random guessing)

Answer

An AUC of 0.50 corresponds to the diagonal line of the ROC curve, meaning the test discriminates no better than flipping a coin — it has no diagnostic value. An AUC of 1.0 is a perfect test; AUC ≥ 0.70 is generally considered acceptable. AUC represents the probability that the test correctly ranks a randomly chosen diseased individual above a randomly chosen non-diseased individual.

EDAIC - European Diploma in Anesthesiology and Intensive Care Practice Test

EDAIC - European Diploma in Anesthesiology and Intensive Care Practice Test

EDAIC Statistics and Evidence-Based Medicine