Relative Risk Calculator

In epidemiology and clinical research, Relative Risk (RR) measures how much more (or less) likely an outcome is in an exposed group compared to a control group — a core statistic for evaluating risk factors and treatments. Enter the events and totals for your exposed and control groups, then select a confidence level to calculate the Relative Risk. Secondary outputs include risk in each group, the confidence interval bounds, and standard error (ln RR).

Number of participants with the outcome in the exposed group

Total number of participants in the exposed group

Number of participants with the outcome in the control group

Total number of participants in the control group

Statistical confidence level for interval calculation

Results

Relative Risk (RR)

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Risk in Exposed Group

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Risk in Control Group

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CI Lower Bound

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CI Upper Bound

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Standard Error (ln RR)

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Frequently Asked Questions

What is relative risk and what is a risk ratio?

Relative risk (RR), also called risk ratio, compares the probability of a disease or outcome occurring in an exposed group versus a control group. It's calculated by dividing the risk in the exposed group by the risk in the control group.

How do I interpret relative risk values?

An RR of 1 means equal risk in both groups. RR > 1 indicates increased risk in the exposed group, while RR < 1 suggests protective effect. For example, RR = 2 means twice the risk in the exposed group.

What is a confidence interval for relative risk?

A confidence interval provides a range of plausible values for the true relative risk. A 95% CI means we're 95% confident the true RR lies within this range. If the CI includes 1, the result may not be statistically significant.

What's the difference between relative risk and odds ratio?

Relative risk compares risks (probabilities) directly, while odds ratio compares odds. RR is more intuitive but requires cohort study data. Odds ratio can be calculated from case-control studies and approximates RR when the outcome is rare.

When should I use relative risk calculations?

Use relative risk for cohort studies where you follow exposed and unexposed groups over time to measure outcome occurrence. It's ideal for evaluating treatment effects, risk factors, or preventive interventions in clinical and epidemiological research.

How is the standard error calculated for relative risk?

The standard error of ln(RR) is calculated using the formula: SE = sqrt(1/a + 1/c - 1/(a+b) - 1/(c+d)), where a, b, c, d represent the four cells of the 2x2 contingency table.

What if I have zero events in one of my groups?

When zero events occur in either group, calculations become problematic. A common approach is to add 0.5 to all four cells of the 2x2 table before calculating, though this should be interpreted cautiously.