Risk Calculation Formulas:
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Absolute risk represents the actual probability of an event occurring in a specific population, while relative risk compares the risk between two groups (typically treated vs control). Understanding both measures provides comprehensive insight into treatment effects and risk assessment.
The calculator uses standard epidemiological formulas:
Where:
Explanation: Absolute risk gives the actual probability, while relative risk shows how much the risk changes between groups. A relative risk of 1 indicates no difference, less than 1 indicates reduced risk, and greater than 1 indicates increased risk.
Details: Both absolute and relative risk measures are essential for clinical decision-making, treatment evaluation, and public health planning. They help quantify treatment benefits and communicate risk effectively to patients and stakeholders.
Tips: Enter event counts and population sizes for both treated and control groups. Ensure all values are positive integers, with population sizes greater than zero. The calculator will compute absolute risks and their ratio (relative risk).
Q1: What's the difference between absolute and relative risk?
A: Absolute risk shows the actual probability, while relative risk shows the ratio between two probabilities. Both are important for complete risk assessment.
Q2: When should I use relative risk vs absolute risk?
A: Use relative risk to compare treatment effects, and absolute risk to understand actual probability. Both should be reported together for comprehensive analysis.
Q3: What does a relative risk of 0.5 mean?
A: A relative risk of 0.5 means the treatment group has half the risk of the control group, indicating a 50% risk reduction.
Q4: How do I interpret absolute risk reduction?
A: Absolute risk reduction is the difference between control and treatment absolute risks. It represents the actual decrease in probability due to the intervention.
Q5: Are there limitations to these risk measures?
A: Yes, they don't account for time-to-event, competing risks, or confounding factors. More sophisticated methods like survival analysis may be needed for complex studies.