Multiple Linear Regression Analysis
Multiple linear regression analysis is an extension of simple linear regression analysis, used to assess the association between two or more independent variables and a single continuous dependent variable. The multiple linear regression equation is as follows:
,
whereis the predicted or expected value of the dependent variable, X_{1} through X_{p} are p distinct independent or predictor variables, b_{0} is the value of Y when all of the independent variables (X_{1} through X_{p}) are equal to zero, and b_{1} through b_{p} are the estimated regression coefficients. Each regression coefficient represents the change in Y relative to a one unit change in the respective independent variable. In the multiple regression situation, b_{1}, for example, is the change in Y relative to a one unit change in X_{1}, holding all other independent variables constant (i.e., when the remaining independent variables are held at the same value or are fixed). Again, statistical tests can be performed to assess whether each regression coefficient is significantly different from zero.
Controlling for Confounding With Multiple Linear Regression
Multiple regression analysis is also used to assess whether confounding exists. Since multiple linear regression analysis allows us to estimate the association between a given independent variable and the outcome holding all other variables constant, it provides a way of adjusting for (or accounting for) potentially confounding variables that have been included in the model.
Suppose we have a risk factor or an exposure variable, which we denote X_{1} (e.g., X_{1}=obesity or X_{1}=treatment), and an outcome or dependent variable which we denote Y. We can estimate a simple linear regression equation relating the risk factor (the independent variable) to the dependent variable as follows:
where b_{1} is the estimated regression coefficient that quantifies the association between the risk factor and the outcome.
Suppose we now want to assess whether a third variable (e.g., age) is a confounder. We denote the potential confounder X_{2}, and then estimate a multiple linear regression equation as follows:
.
In the multiple linear regression equation, b_{1} is the estimated regression coefficient that quantifies the association between the risk factor X_{1} and the outcome, adjusted for X_{2} (b_{2} is the estimated regression coefficient that quantifies the association between the potential confounder and the outcome). As noted earlier, some investigators assess confounding by assessing how much the regression coefficient associated with the risk factor (i.e., the measure of association) changes after adjusting for the potential confounder. In this case, we compare b_{1} from the simple linear regression model to b_{1} from the multiple linear regression model. As a rule of thumb, if the regression coefficient from the simple linear regression model changes by more than 10%, then X_{2} is said to be a confounder.
Once a variable is identified as a confounder, we can then use multiple linear regression analysis to estimate the association between the risk factor and the outcome adjusting for that confounder. The test of significance of the regression coefficient associated with the risk factor can be used to assess whether the association between the risk factor is statistically significant after accounting for one or more confounding variables. This is also illustrated below.
Example  The Association Between BMI and Systolic Blood Pressure
Suppose we want to assess the association between BMI and systolic blood pressure using data collected in the seventh examination of the Framingham Offspring Study. A total of n=3,539 participants attended the exam, and their mean systolic blood pressure was 127.3 with a standard deviation of 19.0. The mean BMI in the sample was 28.2 with a standard deviation of 5.3.
A simple linear regression analysis reveals the following:
Independent Variable 
Regression Coefficient 
T 
Pvalue 
Intercept 
108.28 
62.61 
0.0001 
BMI 
0.67 
11.06 
0.0001 
The simple linear regression model is:
where
is the predicted of expected systolic blood pressure. The regression coefficient associated with BMI is 0.67 suggesting that each one unit increase in BMI is associated with a 0.67 unit increase in systolic blood pressure. The association between BMI and systolic blood pressure is also statistically significant (p=0.0001).
Suppose we now want to assess whether age (a continuous variable, measured in years), male gender (yes/no), and treatment for hypertension (yes/no) are potential confounders, and if so, appropriately account for these using multiple linear regression analysis. For analytic purposes, treatment for hypertension is coded as 1=yes and 0=no. Gender is coded as 1=male and 0=female. A multiple regression analysis reveals the following:
Independent Variable 
Regression Coefficient 
T 
Pvalue 
Intercept 
68.15 
26.33 
0.0001 
BMI 
0.58 
10.30 
0.0001 
Age 
0.65 
20.22 
0.0001 
Male gender 
0.94 
1.58 
0.1133 
Treatment for hypertension 
6.44 
9.74 
0.0001 
The multiple regression model is:
= 68.15 + 0.58 (BMI) + 0.65 (Age) + 0.94 (Male gender) + 6.44 (Treatment for hypertension).
Notice that the association between BMI and systolic blood pressure is smaller (0.58 versus 0.67) after adjustment for age, gender and treatment for hypertension. BMI remains statistically significantly associated with systolic blood pressure (p=0.0001), but the magnitude of the association is lower after adjustment. The regression coefficient decreases by 13%.
[Actually, doesn't it decrease by 15.5%. In this case the true "beginning value" was 0.58, and confounding caused it to appear to be 0.67. so the actual % change = 0.09/0.58 = 15.5%.]
Using the informal rule (i.e., a change in the coefficient in either direction by 10% or more), we meet the criteria for confounding. Thus, part of the association between BMI and systolic blood pressure is explained by age, gender and treatment for hypertension.
This also suggests a useful way of identifying confounding. Typically, we try to establish the association between a primary risk factor and a given outcome after adjusting for one or more other risk factors. One useful strategy is to use multiple regression models to examine the association between the primary risk factor and the outcome before and after including possible confounding factors. If the inclusion of a possible confounding variable in the model causes the association between the primary risk factor and the outcome to change by 10% or more, then the additional variable is a confounder.
Relative Importance of the Independent Variables
Assessing only the pvalues suggests that these three independent variables are equally statistically significant. The magnitude of the t statistics provides a means to judge relative importance of the independent variables. In this example, age is the most significant independent variable, followed by BMI, treatment for hypertension and then male gender. In fact, male gender does not reach statistical significance (p=0.1133) in the multiple regression model.
Some investigators argue that regardless of whether an important variable such as gender reaches statistical significance it should be retained in the model. Other investigators only retain variables that are statistically significant.
[Not sure what you mean here; do you mean to control for confounding?] /WL
This is yet another example of the complexity involved in multivariable modeling. The multiple regression model produces an estimate of the association between BMI and systolic blood pressure that accounts for differences in systolic blood pressure due to age, gender and treatment for hypertension.
A one unit increase in BMI is associated with a 0.58 unit increase in systolic blood pressure holding age, gender and treatment for hypertension constant. Each additional year of age is associated with a 0.65 unit increase in systolic blood pressure, holding BMI, gender and treatment for hypertension constant.
Men have higher systolic blood pressures, by approximately 0.94 units, holding BMI, age and treatment for hypertension constant and persons on treatment for hypertension have higher systolic blood pressures, by approximately 6.44 units, holding BMI, age and gender constant. The multiple regression equation can be used to estimate systolic blood pressures as a function of a participant's BMI, age, gender and treatment for hypertension status. For example, we can estimate the blood pressure of a 50 year old male, with a BMI of 25 who is not on treatment for hypertension as follows:
We can estimate the blood pressure of a 50 year old female, with a BMI of 25 who is on treatment for hypertension as follows:
Evaluating Effect Modification With Multiple Linear Regression
On page 4 of this module we considered data from a clinical trial designed to evaluate the efficacy of a new drug to increase HDL cholesterol. One hundred patients enrolled in the study and were randomized to receive either the new drug or a placebo. The investigators were at first disappointed to find very little difference in the mean HDL cholesterol levels of treated and untreated subjects.

Sample Size 
Mean HDL 
Standard Deviation of HDL 
New Drug 
50 
40.16 
4.46 
Placebo 
50 
39.21 
3.91 
However, when they analyzed the data separately in men and women, they found evidence of an effect in men, but not in women. We noted that when the magnitude of association differs at different levels of another variable (in this case gender), it suggests that effect modification is present.
WOMEN 
Sample Size 
Mean HDL 
Standard Deviation of HDL 
New Drug 
40 
38.88 
3.97 
Placebo 
41 
39.24 
4.21 




MEN 



New Drug 
10 
45.25 
1.89 
Placebo 
9 
39.06 
2.22 
Multiple regression analysis can be used to assess effect modification. This is done by estimating a multiple regression equation relating the outcome of interest (Y) to independent variables representing the treatment assignment, sex and the product of the two (called the treatment by sex interaction variable). For the analysis, we let T = the treatment assignment (1=new drug and 0=placebo), M = male gender (1=yes, 0=no) and TM, i.e., T * M or T x M, the product of treatment and male gender. In this case, the multiple regression analysis revealed the following:
Independent Variable 
Regression Coefficient 
T 
Pvalue 
Intercept 
39.24 
65.89 
0.0001 
T (Treatment) 
0.36 
0.43 
0.6711 
M (Male Gender) 
0.18 
0.13 
0.8991 
TM (Treatment x Male Gender) 
6.55 
3.37 
0.0011 
The multiple regression model is:
The details of the test are not shown here, but note in the table above that in this model, the regression coefficient associated with the interaction term, b_{3}, is statistically significant (i.e., H_{0}: b_{3} = 0 versus H_{1}: b_{3} ≠ 0). The fact that this is statistically significant indicates that the association between treatment and outcome differs by sex.
The model shown above can be used to estimate the mean HDL levels for men and women who are assigned to the new medication and to the placebo. In order to use the model to generate these estimates, we must recall the coding scheme (i.e., T = 1 indicates new drug, T=0 indicates placebo, M=1 indicates male sex and M=0 indicates female sex).
The expected or predicted HDL for men (M=1) assigned to the new drug (T=1) can be estimated as follows:
The expected HDL for men (M=1) assigned to the placebo (T=0) is:
Similarly, the expected HDL for women (M=0) assigned to the new drug (T=1) is:
The expected HDL for women (M=0)assigned to the placebo (T=0) is:
Notice that the expected HDL levels for men and women on the new drug and on placebo are identical to the means shown the table summarizing the stratified analysis. Because there is effect modification, separate simple linear regression models are estimated to assess the treatment effect in men and women:
MEN 
Regression Coefficient 
T 
Pvalue 
Intercept 
39.08 
57.09 
0.0001 
T (Treatment) 
6.19 
6.56 
0.0001 




WOMEN 
Regression Coefficient 
T 
Pvalue 
Intercept 
39.24 
61.36 
0.0001 
T (Treatment) 
0.36 
0.40 
0.6927 
The regression models are:
In Men: 
In Women: 
In men, the regression coefficient associated with treatment (b_{1}=6.19) is statistically significant (details not shown), but in women, the regression coefficient associated with treatment (b_{1}= 0.36) is not statistically significant (details not shown).
Multiple linear regression analysis is a widely applied technique. In this section we showed here how it can be used to assess and account for confounding and to assess effect modification. The techniques we described can be extended to adjust for several confounders simultaneously and to investigate more complex effect modification (e.g., threeway statistical interactions).
There is an important distinction between confounding and effect modification. Confounding is a distortion of an estimated association caused by an unequal distribution of another risk factor. When there is confounding, we would like to account for it (or adjust for it) in order to estimate the association without distortion. In contrast, effect modification is a biological phenomenon in which the magnitude of association is differs at different levels of another factor, e.g., a drug that has an effect on men, but not in women. In the example, present above it would be in inappropriate to pool the results in men and women. Instead, the goal should be to describe effect modification and report the different effects separately.
There are many other applications of multiple regression analysis. A popular application is to assess the relationships between several predictor variables simultaneously, and a single, continuous outcome. For example, it may be of interest to determine which predictors, in a relatively large set of candidate predictors, are most important or most strongly associated with an outcome. It is always important in statistical analysis, particularly in the multivariable arena, that statistical modeling is guided by biologically plausible associations.
"Dummy" Variables in Regression Models
Independent variables in regression models can be continuous or dichotomous. Regression models can also accommodate categorical independent variables. For example, it might be of interest to assess whether there is a difference in total cholesterol by race/ethnicity. The module on Hypothesis Testing presented analysis of variance as one way of testing for differences in means of a continuous outcome among several comparison groups. Regression analysis can also be used. However, the investigator must create indicator variables to represent the different comparison groups (e.g., different racial/ethnic groups). The set of indicator variables (also called dummy variables) are considered in the multiple regression model simultaneously as a set independent variables. For example, suppose that participants indicate which of the following best represents their race/ethnicity: White, Black or African American, American Indian or Alaskan Native, Asian, Native Hawaiian or Pacific Islander or Other Race. This categorical variable has six response options. To consider race/ethnicity as a predictor in a regression model, we create five indicator variables (one less than the total number of response options) to represent the six different groups. To create the set of indicators, or set of dummy variables, we first decide on a reference group or category. In this example, the reference group is the racial group that we will compare the other groups against. Indicator variable are created for the remaining groups and coded 1 for participants who are in that group (e.g., are of the specific race/ethnicity of interest) and all others are coded 0. In the multiple regression model, the regression coefficients associated with each of the dummy variables (representing in this example each race/ethnicity group) are interpreted as the expected difference in the mean of the outcome variable for that race/ethnicity as compared to the reference group, holding all other predictors constant. The example below uses an investigation of risk factors for low birth weight to illustrates this technique as well as the interpretation of the regression coefficients in the model.
Example of the Use of Dummy Variables
An observational study is conducted to investigate risk factors associated with infant birth weight. The study involves 832 pregnant women. Each woman provides demographic and clinical data and is followed through the outcome of pregnancy. At the time of delivery, the infant s birth weight is measured, in grams, as is their gestational age, in weeks. Birth weights vary widely and range from 404 to 5400 grams. The mean birth weight is 3367.83 grams with a standard deviation of 537.21 grams. Investigators wish to determine whether there are differences in birth weight by infant gender, gestational age, mother's age and mother's race. In the study sample, 421/832 (50.6%) of the infants are male and the mean gestational age at birth is 39.49 weeks with a standard deviation of 1.81 weeks (range 2243 weeks). The mean mother's age is 30.83 years with a standard deviation of 5.76 years (range 1745 years). Approximately 49% of the mothers are white; 41% are Hispanic; 5% are black; and 5% identify themselves as other race. A multiple regression analysis is performed relating infant gender (coded 1=male, 0=female), gestational age in weeks, mother's age in years and 3 dummy or indicator variables reflecting mother's race. The results are summarized in the table below.
Independent Variable 
Regression Coefficient 
T 
Pvalue 
Intercept 
3850.92 
11.56 
0.0001 
Male infant 
174.79 
6.06 
0.0001 
Gestational age, weeks 
179.89 
22.35 
0.0001 
Mother's age, years 
1.38 
0.47 
0.6361 
Black race 
138.46 
1.93 
0.0535 
Hispanic race 
13.07 
0.37 
0.7103 
Other race 
68.67 
1.05 
0.2918 
Many of the predictor variables are statistically significantly associated with birth weight. Male infants are approximately 175 grams heavier than female infants, adjusting for gestational age, mother's age and mother's race/ethnicity. Gestational age is highly significant (p=0.0001), with each additional gestational week associated with an increase of 179.89 grams in birth weight, holding infant gender, mother's age and mother's race/ethnicity constant. Mother's age does not reach statistical significance (p=0.6361). Mother's race is modeled as a set of three dummy or indicator variables. In this analysis, white race is the reference group. Infants born to black mothers have lower birth weight by approximately 140 grams (as compared to infants born to white mothers), adjusting for gestational age, infant gender and mothers age. This difference is marginally significant (p=0.0535). There are no statistically significant differences in birth weight in infants born to Hispanic versus white mothers or to women who identify themselves as other race as compared to white.