In statistics and mathematical epidemiology, relative risk (RR) is the risk of an event (or of developing a disease) relative to exposure. Relative risk is a ratio of the probability of the event occurring in the exposed group versus a nonexposed group.^{[1]}
Consider an example where the probability of developing lung cancer among smokers was 20% and among nonsmokers 1%. This situation is expressed in the 2 × 2 table to the right.
Risk  Disease status  

Present  Absent  
Smk  a  b 
Nonsmk  c  d 
Here, a = 20(%), b = 80, c = 1, and d = 99. Then the relative risk of cancer associated with smoking would be
Smokers would be twenty times as likely as nonsmokers to develop lung cancer.
Another term for the relative risk is the risk ratio because it is the ratio of the risk in the exposed divided by the risk in the unexposed.
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Relative risk is used frequently in the statistical analysis of binary outcomes where the outcome of interest has relatively low probability. It is thus often suited to clinical trial data, where it is used to compare the risk of developing a disease, in people not receiving the new medical treatment (or receiving a placebo) versus people who are receiving an established (standard of care) treatment. Alternatively, it is used to compare the risk of developing a side effect in people receiving a drug as compared to the people who are not receiving the treatment (or receiving a placebo). It is particularly attractive because it can be calculated by hand in the simple case, but is also susceptible to regression modelling, typically in a Poisson regression framework.
In a simple comparison between an experimental group and a control group:
As a consequence of the Delta method, the log of the relative risk has a sampling distribution that is approximately normal with variance that can be estimated by a formula involving the number of subjects in each group and the event rates in each group (see Delta method) ^{[2]}. This permits the construction of a confidence interval (CI) which is symmetric around log(RR), i.e.,
where z_{α} is the standard score for the chosen level of significance and SE the standard error. The antilog can be taken of the two bounds of the logCI, giving the high and low bounds for an asymmetric confidence interval around the relative risk.
In regression models, the treatment is typically included as a dummy variable along with other factors that may affect risk. The relative risk is normally reported as calculated for the mean of the sample values of the explanatory variables.
Relative risk is different from the odds ratio, although it asymptotically approaches it for small probabilities. In the example of association of smoking to lung cancer considered above, if a is substantially smaller than b, then a/(a + b) a/b. And if similarly c is smaller enough than d, then c/(c + d) c/d. Thus
This is nothing else but the odds ratio.
In fact, the odds ratio has much wider use in statistics, since logistic regression, often associated with clinical trials, works with the log of the odds ratio, not relative risk. Because the log of the odds ratio is estimated as a linear function of the explanatory variables, the estimated odds ratio for 70yearolds and 60yearolds associated with type of treatment would be the same in a logistic regression models where the outcome is associated with drug and age, although the relative risk might be significantly different. In cases like this, statistical models of the odds ratio often reflect the underlying mechanisms more effectively.
Since relative risk is a more intuitive measure of effectiveness, the distinction is important especially in cases of medium to high probabilities. If action A carries a risk of 99.9% and action B a risk of 99.0% then the relative risk is just over 1, while the odds associated with action A are almost 10 times higher than the odds with B.
In medical research, the odds ratio is favoured for casecontrol studies and retrospective studies. Relative risk is used in randomized controlled trials and cohort studies.^{[3]}
In statistical modelling, approaches like poisson regression (for counts of events per unit exposure) have relative risk interpretations: the estimated effect of an explanatory variable is multiplicative on the rate, and thus leads to a risk ratio or relative risk. Logistic regression (for binary outcomes, or counts of successes out of a number of trials) must be interpreted in oddsratio terms: the effect of an explanatory variable is multiplicative on the odds and thus leads to an odds ratio.
Whether a given relative risk can be considered statistically significant is dependent on the relative difference between the conditions compared, the amount of measurement and the noise associated with the measurement (of the events considered). In other words, the confidence one has, in a given relative risk being nonrandom (i.e. it is not a consequence of chance), depends on the signaltonoise ratio and the sample size.
Expressed mathematically, the confidence that a result is not by random chance is given by the following formula by Sackett^{[4]}:
For clarity, the above formula is presented in tabular form below.
Dependence of confidence with noise, signal and sample size (tabular form)
Parameter  Parameter increases  Parameter decreases 

Noise  Confidence decreases  Confidence increases 
Signal  Confidence increases  Confidence decreases 
Sample size  Confidence increases  Confidence decreases 
In words, the confidence is higher if the noise is lower and/or the sample size is larger and/or the effect size (signal) is increased. The confidence of a relative risk value (and its associated confidence interval) is not dependent on effect size alone. If the sample size is large and the noise is low a small effect size can be measured with great confidence. Whether a small effect size is considered important is dependent on the context of the events compared.
In medicine, small effect sizes (reflected by small relative risk values) are usually considered clinically relevant (if there is great confidence in them) and are frequently used to guide treatment decisions. A relative risk of 1.10 may seem very small, but over a large number of patients will make a noticeable difference. Whether a given treatment is considered a worthy endeavour is dependent on the risks, benefits and costs.
Example 1: risk reduction  Example 2: risk increase  

Experimental group (E)  Control group (C)  Total  (E)  (C)  
Events (E)  EE = 15  CE = 100  115  EE = 75  CE = 100 
Nonevents (N)  EN = 135  CN = 150  285  EN = 75  CN = 150 
Total subjects (S)  ES = EE + EN = 150  CS = CE + CN = 250  400  ES = 150  CS = 250 
Event rate (ER)  EER = EE / ES = 0.1, or 10%  CER = CE / CS = 0.4, or 40%  N/A  EER = 0.5 (50%)  CER = 0.4 (40%) 
Equation  Variable  Abbr.  Example 1  Example 2 

EER − CER  < 0: absolute risk reduction  ARR  (−)0.3, or (−)30%  N/A 
> 0: absolute risk increase  ARI  N/A  0.1, or 10%  
(EER − CER) / CER  < 0: relative risk reduction  RRR  (−)0.75, or (−)75%  N/A 
> 0: relative risk increase  RRI  N/A  0.25, or 25%  
1 / (EER − CER)  < 0: number needed to treat  NNT  (−)3.33  N/A 
> 0: number needed to harm  NNH  N/A  10  
EER / CER  relative risk  RR  0.25  1.25 
(EE / EN) / (CE / CN)  odds ratio  OR  0.167  1.5 
EE / (EE + CE) − EN / (EN + CN)  attributable risk  AR  (−)0.34, or (−)34%  0.095, or 9.5% 
(RR − 1) / RR  attributable risk percent  ARP  N/A  20% 
1 − RR (or 1 − OR)  preventive fraction  PF  0.75, or 75%  N/A 

