## Confounding vs. Bias: Key Distinction **Key Point:** Confounding is a **validity issue** that arises from an extraneous variable associated with both exposure and outcome. Bias is a **systematic error** in measurement or selection that cannot be eliminated by statistical adjustment. ### Distinguishing Feature: Adjustability The critical difference between confounding and bias lies in their **response to stratification and adjustment**: | Feature | Confounding | Bias | |---------|-------------|------| | **Definition** | Extraneous variable distorts true association | Systematic error in measurement or selection | | **Adjustability** | **Eliminated or reduced by stratification/adjustment** | Persists despite adjustment | | **Mechanism** | Associated with both exposure and outcome | Systematic deviation from true value | | **Example** | Smoking confounds coffee–MI association | Misclassification of coffee intake | ### Analysis of This Scenario 1. **Initial finding:** RR = 2.5 (coffee → MI) 2. **After controlling for smoking:** RR = 1.1 (no longer significant) 3. **Interpretation:** The association was **confounded by smoking** - Smoking is associated with both high coffee consumption AND MI risk - When smoking is removed, the true (null) association emerges **High-Yield:** The **hallmark of confounding is that it disappears or substantially weakens when the confounder is stratified or adjusted for**. This is how you distinguish it from bias in an exam scenario. **Clinical Pearl:** In real-world epidemiology, confounding is often more common than bias in observational studies and is the reason we use multivariate regression, propensity score matching, and stratified analysis. [cite:Park 26e Ch 8]
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