As I understand the use of a risk ratio, the ratio is multiplied by the average risk of the local population to obtain the risk of a person. This must surely be wrong. Because it implies that the meaning of the raw score is a variable that changes with the population, and it assumes that the SHAPE of the risk distribution does not change from one population to another. Alright, my assumption must wrong. How is a risk ratio to be used, and has the method been validated?
Pritha Chaturvedi
Risk Ratios are Multiplicative, Not Additive:5%×2=10%5\% \times 2 = 10\%5%×2=10% If an individual in a reference population has a baseline risk of 5%, and the risk ratio is 2.0, then their estimated risk in the exposed group would be:10%×2=20%10\% \times 2 = 20\%10%×2=20% However, if the baseline risk in another population is higher (e.g., 10%), then applying the same RR gives: This shows that the absolute risk estimates changes depending on the baseline risk of the local population. Risk Ratio Assumes Similar Proportional Effects: The assumption behind applying an RR across populations is that the proportional increase in risk is constant across different baseline risks. This assumption does not always hold because underlying risk factors (e.g., genetics, environmental exposures) may differ between populations, altering the shape of the risk distribution. Validation of the Risk Ratio Approach Risk Risk ratios are generally derived from epidemiological studies, and their validity depends on how well the study population matches the population to which you want to apply them. External validation studies are needed to check whether an RR from one population generalizes to another. Some methods adjust for different baseline risks using absolute risk models rather than relying solely on RR.
Interpreting the Risk Ratio in a Different Population
Potential Issues with Applying Risk Ratios
- Baseline risk differences: If two populations have different baseline risks, the absolute impact of an exposure will differ.
- Non-linearity in effects: The RR may not apply uniformly across different risk levels (e.g., a risk factor may have a stronger effect in high-risk individuals).
- Interaction effects: Other risk factors in a different population may modify the effect of the exposure, making the RR from one study inapplicable elsewhere.
Alternative Approaches
- Absolute Risk Models: These integrate multiple factors rather than applying a single RR.
- Calibration Studies: Comparing predicted risks using an RR against observed risks in the target population can validate its application
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