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Relative Risk Reduction Formula:
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Relative Risk Reduction (RRR) is a measure of the reduction in risk achieved by an experimental intervention compared to a control intervention. It expresses the proportional reduction in risk between the experimental and control groups.
The calculator uses the RRR formula:
Where:
Explanation: The formula calculates the proportional reduction in risk by comparing the difference between control and experimental event rates relative to the control event rate.
Details: RRR is widely used in clinical trials and epidemiological studies to quantify the effectiveness of interventions, treatments, or preventive measures. It helps healthcare professionals and researchers understand the relative benefit of new treatments compared to standard care or placebo.
Tips: Enter both Control Event Rate (CER) and Experimental Event Rate (EER) as percentages. Values must be between 0 and 100. CER must be greater than 0 for the calculation to be valid.
Q1: What is the difference between RRR and ARR?
A: RRR shows the proportional reduction in risk, while ARR (Absolute Risk Reduction) shows the absolute difference in risk. RRR is often larger and can be more impressive, but ARR provides the actual clinical impact.
Q2: When is RRR most useful?
A: RRR is particularly useful when baseline risks are different across studies or populations, as it provides a standardized measure of treatment effect that can be compared across different contexts.
Q3: What are the limitations of RRR?
A: RRR can be misleading when baseline risks are low, as small absolute differences can appear as large relative reductions. It's important to consider both RRR and ARR for complete understanding.
Q4: How is RRR interpreted in clinical practice?
A: A higher RRR indicates greater treatment effectiveness. For example, an RRR of 50% means the treatment reduces the risk of the outcome by half compared to the control.
Q5: Can RRR be negative?
A: Yes, if the experimental event rate is higher than the control event rate, RRR will be negative, indicating that the experimental intervention increases risk compared to control.