## Study Design & Measure Selection **Key Point:** In a cross-sectional study, both exposure and outcome are measured simultaneously at a single point in time. The temporal sequence cannot be established, making relative risk inappropriate. ### Why Odds Ratio is Correct **High-Yield:** Odds Ratio is the measure of association for: - Cross-sectional studies - Case-control studies - Logistic regression models Odds Ratio compares the odds of disease in the exposed group to the odds of disease in the unexposed group: $$OR = \frac{\text{Odds of disease in exposed}}{\text{Odds of disease in unexposed}} = \frac{a \times d}{b \times c}$$ where a = exposed with disease, b = exposed without disease, c = unexposed with disease, d = unexposed without disease. ### Comparison of Measures by Study Design | Study Design | Appropriate Measure | Reason | |---|---|---| | **Cohort (Prospective)** | Relative Risk (RR) | Temporal sequence established; can calculate incidence | | **Case-Control** | Odds Ratio (OR) | Starts with outcome; cannot calculate true incidence | | **Cross-Sectional** | Odds Ratio (OR) | No temporal sequence; exposure & outcome simultaneous | | **Experimental/RCT** | Relative Risk (RR) | Prospective; clear cause-effect timeline | **Clinical Pearl:** Although OR approximates RR when the disease is rare (< 10% prevalence), in this factory survey with potentially higher prevalence of respiratory disease, OR is the methodologically correct choice. ### Why Other Measures Don't Fit **Warning:** Relative Risk requires a clear temporal sequence where exposure precedes outcome. In cross-sectional studies, you cannot determine who was exposed first or who developed disease first—both are assessed simultaneously. **Mnemonic: CORC** — **C**ase-control and **O**dds **R**atio go together; **C**ohort and **R**elative **R**isk go together.
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