Relative risk (RR) compares the probability of disease in an exposed group versus an unexposed group: RR = Risk(Exposed) / Risk(Unexposed). An RR > 1 indicates increased risk, RR = 1 indicates no association, and RR < 1 indicates decreased risk. It is the primary measure of effect in cohort and experimental studies.
Work with 2×2 tables from published cohort studies, calculating incidence in each exposure group, then compute and interpret the ratio. Practice with various RR values and confidence intervals to understand clinical significance.
RR ≠ OR; RR of 2 does not mean twice as many people will develop disease, only that the rate is twice as high; CI not crossing 1 indicates statistical significance but not necessarily clinical importance.
From your study of disease frequency measures, you know how to calculate cumulative incidence (risk) — the proportion of a defined population that develops disease over a specified time period. From measures of association, you know the conceptual goal: comparing disease frequency between exposed and unexposed groups. Relative risk (also called the risk ratio) is the most direct expression of that comparison: RR = Risk(Exposed) / Risk(Unexposed). If 10% of smokers develop lung disease over 20 years and 1% of non-smokers do, the RR is 10/1 = 10 — smokers face ten times the risk. The ratio is interpretable on its own scale: an RR of 1.0 means identical risk in both groups (no association); above 1.0 means the exposure increases risk; below 1.0 means it decreases risk (a protective association).
The 2×2 table is the calculation engine. Label the rows exposed/unexposed and the columns diseased/not-diseased. The cells are conventionally called a (exposed and diseased), b (exposed and not diseased), c (unexposed and diseased), and d (unexposed and not diseased). Risk in the exposed group is a/(a+b); risk in the unexposed group is c/(c+d). RR = [a/(a+b)] / [c/(c+d)]. Notice what this requires: you need to know the denominator for each exposure group — how many people were at risk — which means RR is calculable in cohort studies (where you follow exposed and unexposed people forward in time) and randomized trials, but not in case-control studies (where cases and controls are sampled after the fact, destroying the natural denominators).
Understanding why RR differs from the odds ratio (OR) is critical for reading literature accurately. The OR compares odds rather than probabilities: OR = (a/b) / (c/d) = ad/bc. When disease is rare (incidence < 10%), odds ≈ probabilities, so OR ≈ RR — this is the rare disease assumption that makes OR from case-control studies a reasonable approximation of RR. When disease is common, OR diverges from RR substantially, and OR always exaggerates the association away from 1.0 relative to RR. An OR of 3.0 for a common outcome does not mean the same thing as an RR of 3.0. Many published meta-analyses and logistic regression studies report ORs — knowing when they approximate RR and when they don't is a foundational critical appraisal skill.
Interpreting a computed RR requires pairing it with a confidence interval and a baseline risk. Statistical significance (CI excluding 1.0) and clinical significance are separate questions. An RR of 1.5 with baseline risk of 0.01% means the absolute risk increase is 0.005% — clinically negligible despite the relative elevation. Conversely, an RR of 1.2 on a baseline risk of 30% means an absolute risk increase of 6 percentage points — clinically meaningful despite the modest ratio. The absolute risk reduction and number needed to treat (which you will study next) translate RR into the terms most useful for clinical and policy decisions.