## Distinguishing RCTs from Cohort Studies ### Key Design Difference **Key Point:** Random allocation of participants to intervention or control groups is the defining feature that distinguishes RCTs from all observational designs, including cohort studies. ### Comparison Table | Feature | RCT | Cohort Study | | --- | --- | --- | | **Allocation method** | Random (investigator-controlled) | Non-random (participant/exposure-determined) | | **Prospective follow-up** | Yes | Yes | | **Baseline measurements** | Yes | Yes | | **Confounding control** | Randomization balances known & unknown confounders | Matching, stratification, multivariable analysis | | **Causal inference strength** | Strongest (Level 1 evidence) | Moderate (Level 2 evidence) | ### Why Randomization Matters 1. **Balances known confounders** — Random allocation distributes measured risk factors equally between groups. 2. **Balances unknown confounders** — Unlike matching or stratification, randomization protects against confounders not yet identified or measured. 3. **Eliminates selection bias** — Participants cannot self-select into treatment or control based on prognosis or preference. **High-Yield:** Randomization is the **gold standard** for causal inference because it approximates the counterfactual ideal: "What would have happened to the treated group if they had not been treated?" ### Clinical Pearl A cohort study may be prospective and measure outcomes at baseline and follow-up, but without randomization it remains observational and susceptible to confounding bias — even with matching, residual confounding persists. **Mnemonic:** **RCT = Randomization + Control** — The "R" stands for randomization, not retrospective.
Sign up free to access AI-powered MCQ practice with detailed explanations and adaptive learning.