The relative risk compares the risks of two groups.
Use the relative risk to assess the risk between different values of the predictor variables. Minitab displays a separate table of relative risks for categorical and continuous variables.
In the Relative Risks for Categorical Predictors table, Minitab sets up the comparison by listing the levels in 2 columns, Level A and Level B. The relative risk describes the change in how likely the event is to occur for level A relative to level B. Relative risks that are greater than 1 indicate that the event is more likely at level A. Relative risks that are less than 1 indicate that the event is less likely at level A. For example, in the following results the risk of experiencing the event for patients in stage IV is 5.5 times higher than the risk for patients in Stage I.
In the Relative Risks for Continuous Predictors table, Minitab displays the unit of change and the relative risk. The relative risk describes the change in how likely the event is to occur for one unit of change. Relative risks that are greater than 1 indicate that the event is more likely to occur as the predictor increases. Relative risks that are less than 1 indicate that the event is less likely to occur as the predictor increases. For example, in the following results a patient is 1.02 times more likely to experience the event for each increase of 1 year to their age.
Unit of Change | Relative Risk | 95% CI | |
---|---|---|---|
Age | 1 | 1.0192 | (0.9911, 1.0481) |
Level A | Level B | Relative Risk | 95% CI |
---|---|---|---|
Stage | |||
II | I | 1.1503 | (0.4647, 2.8477) |
III | I | 1.9010 | (0.9459, 3.8204) |
IV | I | 5.5068 | (2.4086, 12.5901) |
III | II | 1.6526 | (0.6819, 4.0049) |
IV | II | 4.7872 | (1.7825, 12.8566) |
IV | III | 2.8968 | (1.2952, 6.4788) |
These confidence intervals (CI) are ranges of values that are likely to contain the true values of the relative risks.
Because samples are random, two samples from a population are unlikely to yield identical confidence intervals. However, if you take many random samples, a certain percentage of the resulting confidence intervals contain the unknown population parameter. The percentage of these confidence intervals that contain the parameter is the confidence level of the interval.
Use the confidence interval to assess the estimate of the relative risks. For example, with a 95% confidence level, you can be 95% confident that the confidence interval contains the value of the relative risk for the population. A confidence interval that contains 1 indicates that you cannot determine that the variable has an effect for the specified level of confidence. If a confidence interval does not contain 1, the confidence interval helps you assess the practical significance of your results. Use your specialized knowledge to determine whether the confidence interval includes values that have practical significance for your situation. If the interval is too wide to be useful, consider increasing your sample size.