For some predictors, the default relative risk for a change of 1 unit in the predictor is not useful. For example, if the relative risk for a change of 1 day is too small, enter 365 to see the relative risk for a change of 1 year instead.
Continuous predictor
Shows all the names of the continuous predictors in your model. This column does not take any input.
Increment
Enter the amount of change in the continuous predictor that Minitab uses to calculate the relative risk.
Coding for categorical variables
Coding for categorical predictors
To perform the analysis, Minitab needs to recode the categorical predictors using one of two methods. Consider changing the method based on whether you want to compare the levels of the predictor to a baseline or to a reference level. The coding scheme does not change the test of the overall effect of the predictor. For more information, go to Coding schemes for categorical predictors.
(-1, 0, +1): Choose to compare the risk that an individual in one grouping level experiences the event relative to an individual in a baseline level.
(1, 0): Choose to compare the risk that an individual in one grouping level experiences the event relative to an individual in the reference grouping level. If you choose the (1, 0) coding scheme, the reference level table becomes active in the dialog box.
Reference level table
Categorical predictor
This column of the table shows all the names of the categorical predictors in your model. This column does not take any input.
Reference level
Minitab compares the non-reference levels to the reference level. Changing the reference level does not affect the overall significance, but it can make the interpretation of the regression coefficients more meaningful.
For example, a categorical predictor about whether a patient's platelet count is normal has levels "Yes" and "No." The response event is that a patient dies.
The reference level is in the denominator of the relative risk. When you change the reference level, the relative risk inverts. When the reference level is "No," the relative risk follows:
The relative risk of 0.2 indicates that the patient is 0.2 times as likely to die when the factor is "Yes" as when the factor is "No."
When the reference level is "Yes", the relative risk follows:
The relative risk of 5 indicates that the patient is 5 times as likely to die when the factor is "No" as when the factor is "Yes."
When you change the reference level, the sign of the coefficient changes. When the reference level is "No", the coefficient is -1.6. The negative coefficient indicates that the patient is more likely to die at the reference level of the factor. When the reference level is "Yes", the sign of the coefficient changes to become 1.6. The positive coefficient indicates that the patient is less likely to die at the reference level of the factor.
Standardize continuous predictors
You can choose to standardize the continuous predictors in your model. The standardized predictors are only used to fit the model and are not stored in the worksheet.
Standardizing the continuous predictors can improve the interpretation of the model for specific conditions.
Center the continuous predictors by subtracting the mean: This method helps reduce multicollinearity, which improves the precision of the coefficient estimates. This method is helpful when your model contains highly correlated predictors, higher-order terms, and interaction terms. Each coefficient represents the expected change in the response given a one unit change in the predictor, using the original measurement scale.
Standardize the scale of the continuous predictors by dividing by the standard deviation: This method makes the ranges of the predictors more homogenous so that you can compare the size of the coefficients. This approach is helpful when you want to know which predictors have a larger effect, while controlling for differences in scale. However, each coefficient represents the expected change in the response given a change of one standard deviation in the predictor.
Use one of the following methods to standardize your continuous predictors:
Do not standardize: Use your original data for the continuous predictors.
Specify low and high levels to code as -1 and +1: Use to both center the predictors and to place them on a comparable scale. Minitab uses this method in design of experiments (DOE). All data values that fall between the low and high values that you specify are transformed to fall between −1 and +1. In the table, enter low and high values or use the default minimum and maximum values in the sample.
Continuous predictor
Shows the names of all the continuous predictors in your model. This column does not take any input.
Low
Enter a value to code as −1. The default is the minimum value in the sample.
High
Enter a value to code as +1. The default is the maximum value in the sample.
Subtract the mean, then divide by the standard deviation: Use to both center the predictors and to place them on a comparable scale.
Subtract the mean: Use to center the predictors.
Divide by the standard deviation: Use a comparable scale for all predictors.
Subtract a specified value, then divide by another: Specify other values rather than using the mean and standard deviation estimates from the sample.
Continuous predictor
Shows the names of all the continuous predictors in your model. This column does not take any input.
Subtract
Enter the value to subtract from each continuous predictor.
Divide By
Enter the value that Minitab uses to divide the result of the subtraction.
When the reference level is "Yes", the relative risk follows:
