[This example involves a cross-sectional study of HDL cholesterol (HCHOL, the so-called good cholesterol) and body mass index (BMI), a measure of obesity. Since both BMI and HDL cholesterol will be related to total cholesterol (CHOL), it would make good sense to adjust for total cholesterol.]
In the multiple regression models we have been considering so far, the effects of the predictors have been additive. When HDL cholesterol is regressed on total cholesterol and BMI, the fitted model is
Dependent Variable: HCHOL Parameter Standard T for H0: Variable DF Estimate Error Parameter=0 Prob > |T| INTERCEPT 1 64.853 8.377 7.742 0.000 BMI 1 -1.441 0.321 -4.488 0.000 CHOL 1 0.068 0.027 2.498 0.014says that the expected difference in HCHOL is 0.068 per unit difference in CHOL when BMI is held fixed. This is true whatever the value of BMI. The difference in HCHOL is -1.441 per unit difference in BMI when CHOL is held fixed. This is true whatever the value of CHOL. The effects of CHOL and BMI are additive because the expected difference in HDL cholesterol corresponding to differences in both CHOL and BMI is obtained by adding the differences expected from CHOL and BMI determined without regard to the other's value. That is, the expected difference between two subjects' HDL is
The model that was fitted to the data (HCHOL = b0 + b1 CHOL + b2 BMI ) forces the effects to be additive, that is, the effect of CHOL is the same for all values of BMI and vice-versa because the model won't let it be anything else. While this condition might seem restrictive, experience shows that it is a satisfactory description of many data sets. I'd guess it depends on your area of application. The good news is that we don't have to accept this blindly. There are ways to check on the adequacy of the model!
Even if additivity is appropriate for many situations, there are times when it does not apply. Sometimes, the purpose of a study is to formally test whether additivity holds. Perhaps the way HDL cholesterol varies with BMI depends on total cholesterol. One way to investigate this is by including an interaction term in the model. Let BMICHOL=BMI*CHOL, the product of BMI and CHOL. The model incorporating the interaction is
Dependent Variable: HCHOL Parameter Standard T for H0: Variable DF Estimate Error Parameter=0 Prob > |T| INTERCEPT 1 -24.990 38.234 -0.654 0.515 BMI 1 2.459 1.651 1.489 0.139 CHOL 1 0.498 0.181 2.753 0.007 BMI*CHOL 1 -0.019 0.008 -2.406 0.018The general form of the model is
It can be rewritten two ways to show how the change in response with one variable depends on the other.
(1) Y = b0 + b1 X + (b2 + b3
(2) Y = b0 + b2 Z + (b1 + b3 Z) X
Expression (1) shows the difference in Y per unit difference in Z when X is held fixed is (b2+b3X). This varies with the value of X. Expression (2) shows the difference in Y per unit difference in X when Z is held fixed is (b1+b3Z). This varies with the value of Z. The coefficient b3 measures the amount by which the change in response with one predictor is affected by the other predictor. If b3 is not statistically significant, then the data have not demonstrated the change in response with one predictor depends on the value of the other predictor. In the HCHOL, COL, BMI example, the model
HCHOL = -24.990 + 0.498 CHOL + (2.459
- 0.019 CHOL) BMI or
HCHOL = -24.990 + 2.459 BMI + (0.498 - 0.019 BMI) CHOL
Comment: Great care must be exercised when interpreting the coefficients of individual variables in the presence of interactions. The coefficient of BMI is 2.459. If there were no interaction term, this would be interpreted as saying that among those with a given total cholesterol level, those with greater BMIs are expected to have greater HDL levels! However, once the interaction is taken into account, the coefficient for BMI is, in fact, (2.459-0.019 CHOL), which is negative provided total cholesterol is greater than 129, which is true of all but 3 subjects. (How many people do you know with total cholesterol levels less than 129? There are too many people whose LDL--the so-called "bad cholesterol"--alone is greater than 129!)
Comment: The inclusion of interactions when the study was not
specifically designed to assess them can make it difficult to estimate
the other effects in the model. Therefore, if
it is best to refit the model without
the interaction so other effects might be better assessed.
Interactions have a special interpretation when one of the predictors is a categorical variable with two categories. Consider an example in which the response Y is predicted from a continuous predictor X and indicator of sex (M0F1, =0 for males and 1 for females). The model
Y = b0 + b1 X + b2 M0F1
specifies two simple linear regression equations. For men, M0F1=0 and
The change in Y per unit change in X--b1--is the same for men and women. The model forces the regression lines to be parallel. The difference between men and women is the same for all values of X and is equal to b2, the difference in Y-intercepts.
Including a sex-by-X interaction term in the model allows the regression lines for men and women to have different slopes.
Y = b0 + b1 X + b2 M0F1 + b3 X * M0F1
For men, the model reduces to Y = b0 + b1
while for women, it is Y = (b0 + b2) + (b1 + b3) X
Thus, b3 is the difference in slopes. The slopes for men and women will have been shown to differ if and only if b3 is statistically significant.
The individual regression equations for men and women obtained from the multiple regression equation with a sex-by-X interaction are identical to the equations that are obtained by fitting a simple linear regression of Y on X for men and women separately. The advantage of the multiple regression approach is that it simplifies the task of testing whether the regression coefficients for X differ between men and women.
Comment: A common mistake is to compare two groups by fitting separate regression models and declaring them different if the regression coefficient is statistically significant in one group and not the other. it may be the two regression coefficients are similar with P values close to and on either side of 0.05. In order to show men and women response differently to a change in the continuous predictor, the multiple regression approach must be used and the difference in regression coefficients as measured by the sex-by-X interaction must be tested formally.
Centering refers to the practice of subtracting a constant from predictors before fitting a regression model. Often the constant is a mean, but it can be any value.
There are two reasons to center. One is technical. The numerical routines that fit the model are often more accurate when variables are centered. Some computer programs automatically center variables and transform the model back to the original variables, all without the user's knowledge.
The second reason is practical. The coefficients from a centered model are often easier to interpret. Consider the model that predicts HDL cholesterol from BMI and total cholesterol and a centered version fitted by subtracting 22.5 from each BMI and 215 from each total cholesterol.
In the original model
When there is an interaction in the model,