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Section 15 — Biostatistics & Evidence-Based Medicine Board-prep reference v1.0 · July 2026

Chapter 15.1 — Study Designs & Levels of Evidence

Matching the design to the question · A board-prep reference for reading the neonatal literature

Educational reference. These are statistical and evidence concepts for exam preparation, not patient-specific clinical instructions.
QUICK-REFERENCE BOX — Which Design Answers Which Question?

1. Overview

The Core Idea

Study design determines what you can legitimately conclude. Experimental designs — where the investigator assigns the exposure — support causal inference. Observational designs describe associations but are more vulnerable to bias and confounding. Knowing the design tells you, at a glance, how much weight a result deserves.

2. Why It Matters

Bedside Relevance

Neonatology changes practice on the strength of trials (surfactant, antenatal steroids, therapeutic hypothermia, caffeine). Being able to classify a study and judge its design is what lets you separate a practice-changing RCT from a hypothesis-generating case series.

3. Experimental Designs

The Randomized Controlled Trial
  • The investigator randomly assigns the intervention, which balances known and unknown confounders across groups.
  • Randomization + blinding + intention-to-treat analysis make the RCT the reference standard for therapy questions.
  • Variants: parallel-group, crossover, cluster-randomized, and non-inferiority trials.

4. Observational Designs

DesignDirectionYieldsMain weakness
CohortExposure → outcome (forward)Incidence, relative riskConfounding; long/expensive if prospective
Case-controlOutcome → exposure (backward)Odds ratio; efficient for rare outcomesRecall and selection bias
Cross-sectionalExposure & outcome at oncePrevalenceNo temporality (chicken-or-egg)
Case series/reportDescriptiveHypothesesNo comparison group

5. Evidence Hierarchy

High to Low
  1. Systematic reviews / meta-analyses of RCTs
  2. Individual randomized controlled trials
  3. Cohort studies
  4. Case-control studies
  5. Cross-sectional studies
  6. Case series / case reports
  7. Expert opinion / bench research

6. Matching Design to Question

Question typeBest design
Does this treatment work?Randomized controlled trial
What is the prognosis / incidence?Cohort study
What caused this rare disease?Case-control study
How common is it now?Cross-sectional study
What does all the evidence say?Systematic review / meta-analysis

7. Design Pitfalls

Watch For
  • Cross-sectional data used to claim causation (no temporality).
  • Case-control studies over-interpreted despite recall/selection bias.
  • Uncontrolled case series presented as evidence of effectiveness.
  • Observational associations treated as if they were randomized comparisons.

8. Key Pearls

High-Value Points
  • Only randomization balances unknown confounders — its defining advantage.
  • Case-control = start with the outcome (good for rare diseases) → odds ratio.
  • Cohort = start with the exposure → relative risk and incidence.
  • Cross-sectional = a snapshot → prevalence, no cause-and-effect.
  • The design caps the conclusion — a great analysis can't rescue a weak design.

9. Common Mistakes to Avoid

Misreadings & Better Practice

Frequent errors in interpreting study design.

MistakeWhy it's wrongBetter reading
"Cross-sectional shows X causes Y."No temporality.It shows association/prevalence only.
Reporting relative risk from a case-control study.Case-control yields odds ratios.Use OR (approximates RR if outcome rare).
Treating a case series as proof of efficacy.No control group.Hypothesis-generating only.

10. Board-Style High-Yield Summary

Key Takeaways
  • RCT (randomization) supports causal inference; observational studies show association.
  • Cohort → RR/incidence; case-control → OR (rare outcomes); cross-sectional → prevalence.
  • Evidence hierarchy tops out at systematic reviews/meta-analyses of RCTs.
  • Match the design to the question type.
  • Design determines the strength of the conclusion.

11. References

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