Normal Foundations of Biostatistics & Epidemiology

CPR APPLICATIONS FOUNDATION · CHAPTER 20 · NORMAL

Normal Foundations of Biostatistics & Epidemiology

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Supplemental foundation map: this page orients the detailed CPR Applications for Chapter 20. It is not part of the canonical CPR object inventory.

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20.1 · SOURCE CPR FOUNDATION

Concept

Biostatistics is clinical reasoning under uncertainty. Step 1 rarely tests math in isolation. It tests whether you can interpret a test result, study abstract, confidence interval, graph, or public-health scenario without false reassurance, false alarm, or causal overreach.

Pattern

Step 1 biostatistics is usually embedded inside clinical stems. The question may show a screening result, a new diagnostic test, an RCT abstract, an outbreak table, a survival curve, or a conclusion that sounds stronger than the data support. The task is to decide what the result really means.

Rule

  1. Ask what kind of uncertainty is present. Diagnostic uncertainty, statistical uncertainty, causal uncertainty, and public-health uncertainty are different.
  2. Match the metric to the question. Test performance, predictive value, inference, bias, risk, or decision-making each uses different logic.
  3. Do not overinterpret the result. A significant p-value, positive screen, or impressive relative risk may still mislead.

Applications

  • Positive screen in rare disease → PPV trap
  • Wide confidence interval → imprecise estimate
  • Nonsignificant underpowered study → may be inconclusive; assess confidence interval and statistical power rather than assuming equivalence
  • Statistically significant tiny effect → clinical irrelevance
  • Subgroup-only apparent benefit → consider genuine effect modification but evaluate prespecification, interaction testing, and multiplicity
  • Observational association → causal inference caution
  • Tip: Biostatistics questions usually ask what the result means, not what number you can calculate

CPR APPLICATIONS · ACTIVE REASONING

Understand predictive value, uncertainty, and absolute risk

Commit to an answer before requesting feedback. Full worked explanations remain available if scripts are unavailable. Answers are not transmitted or stored.

Decision 1: A screening test has 90% sensitivity and 90% specificity. In a population with disease prevalence of 1%, approximately what percentage of positive tests represent true disease?

Reveal the worked answer and changed-variable challenge

Best answer: About 8%. Among 1,000 people, about 10 have disease: 9 true positives and 99 false positives, so PPV = 9/108 ≈ 8.3%.

Transfer: How does PPV change if prevalence rises to 10% while sensitivity and specificity remain unchanged?

Decision 2: In a randomized trial, the event rate is 20% with control and 15% with treatment. What is the absolute risk reduction?

Reveal the worked answer and changed-variable challenge

Best answer: 5 percentage points. Absolute risk reduction is 20% minus 15% = 5 percentage points; NNT over this study period is 1/0.05 = 20.

Transfer: How would the NNT change if baseline risk were much lower but relative risk reduction stayed fixed?

Decision 3: An effect estimate has a 95% confidence interval for relative risk of 0.85 to 1.15. Which conclusion best fits?

Reveal the worked answer and changed-variable challenge

Best answer: The estimate is compatible with modest benefit, no effect, and modest harm. The interval includes a risk ratio of 1.0 and thus cannot exclude those possibilities at the reported confidence level.

Transfer: What would a narrower interval entirely below 1 imply, assuming a well-designed unbiased study?

Change the mechanism

Read all comparisons without scripts
  • Low prevalence (1%): Sensitivity 90% / specificity 90% gives PPV ≈8.3% at prevalence 1%.
  • Moderate prevalence (10%): With the same test, PPV = 50%; changing prevalence changes the mix of true and false positives.
  • Higher prevalence (25%): With the same test, PPV = 75%.

Screening test simulator: prevalence changes PPV

Assume sensitivity 90%, specificity 90%, and 1,000 screened people. Change prevalence while keeping the test characteristics fixed.

Worked values if the calculator is unavailable
PrevalenceTrue positivesFalse positivesPPV
1%9998.3%
10%909050.0%
25%2257575.0%

Explain it back: Identify the mechanism, name the most important competing explanation, and predict what changes when the context shifts.

Unscored educational reasoning. Not a validated assessment or individualized clinical recommendation.

How to use this foundation map

  1. Identify the organizing mechanism.
  2. Recognize the pattern.
  3. Restate the rule without looking.
  4. Test transfer with applications and question practice.

Pair CPR with the 5P Approach™ when solving cases and questions.