Task 2a: How to Use SUDAAN Code to Perform Linear Regression

In this example, you will assess the association between high density lipoprotein (HDL) cholesterol — the outcome variable — and body mass index (bmxbmi) — the exposure variable — after controlling for selected covariates in NHANES 1999-2002. These covariates include gender (riagendr), race/ethnicity (ridreth1), age (ridageyr), smoking (smoker, derived from SMQ020 and SMQ040; smoker =1 if non-smoker, 2 if past smoker and 3 if current smoker) and education (dmdeduc).

 

Step 1: Specify the variables in the model

For continuous variables, you have a choice of using the variable in its original form (continuous) or changing it into a categorical variable (e.g. based on standard cutoffs, quartiles or common practice).  The categorical variables should reflect the underlying distribution of the continuous variable and not create categories where there are only a few observations. 

It is important to exam the data both ways, since the assumption that a dependent variable has a continuous relationship with the outcome may not be true.  Looking at the categorical version of the variable will help you to know whether this assumption is true. 

In this example, you could look at BMI as a continuous variable or convert it into a categorical variable based on standard BMI definitions of underweight, normal weight, overweight and obese.  Here is how categorical BMI variables are created:


Info iconIMPORTANT NOTE

These programs use variable formats listed in the Tutorial Formats page. You may need to format the variables in your dataset the same way to reproduce results presented in the tutorial.

 

Table of code to generate categorical BMI and eligibility variables
Code to generate categorical BMI variables Category

if 0 le bmxbmi lt 18.5 then bmicat= 1 ;

underweight

else if 18.5 le bmxbmi lt 25 then bmicat= 2 ;

normal weight

else if 25 le bmxbmi lt 30 then bmicat= 3 ;

overweight

else if bmxbmi ge 30 then bmicat= 4 ;

obese

if (lbdhdl^= . and riagendr^= . and ridreth1^= . and
ridageyr^=. and smoker^= . and dmdeduc^= <. and bmxbmi^= . )
and wtmec4yr>0 and (ridageyr>= 20 ) then eligible= 1 ;

eligibility

 

Step 2: Create a simple linear regression

The association between the dependent and independent variables is expressed using the model statement in the in proc regress procedure. The dependent variable must be a continuous variable and will always appear on the left hand side of the equation. The variables on the right hand side of the equation are the independent variables and may be discrete or continuous.

Discrete variables are specified using a subgroup or a class statement. In proc regress, the dependent variable is NEVER specified in a subgroup or a class statement because it must be a continuous variable.

 


Info iconIMPORTANT NOTE

These programs use variable formats listed in the Tutorial Formats page. You may need to format the variables in your dataset the same way to reproduce results presented in the tutorial.

Option 1. SUDAAN proc regress Procedure for Simple Linear Regression
Statements Explanation
proc sort data =analysis_data; by sdmvstra sdmvpsu; run ;

Use the proc sort procedure to sort the data by strata and primary sampling units (PSU) before running the procedure.
proc regress data=analysis_data; 

Use the SUDAAN procedure, proc regress, to run multiple regression.

subpopn eligible=1 ; 

Use the subpop eligible=1 statement to restrict the analysis to individuals with complete data for all the variables used in the final multiple regression model.

Because only those 20 years and older are of interest in this example, use the subpopn statement to select this subgroup. Please note that for accurate estimates, it is preferable to use subpopn in SUDAAN to select a subgroup for analysis, rather than select the study subgroup in the SAS program while preparing the data file.

nest sdmvstra sdmvpsu; 

Use the nest statement to apply design-based methods of analysis.
weight wtmec4yr; 

Use the weight statement to account for differential selection probabilities and to adjust for non-response. In this example, the examination weight for 4 years of data (wtmec4yr) is used. (For more information on how to select the correct weight for your analysis, see the Weighting module, Task 1.) 

model lbdhdl= bmxbmi; 

Use the model statement to define the associations to be assessed. Specify the dependent variable to the left-hand side of the equation and the independent variable on the right. This model will show the relationship between a unit increase in BMI and cholesterol level.
run ;  

 

Option 2. SUDAAN proc regress Procedure for Simple Linear Regression with Categorical BMI Variable
Statements Explanation
proc regress data=analysis_data;
subpopn eligible=1 ;
nest sdmvstra sdmvpsu;
weight wtmec4yr;
model lbdhdl= bmicat;
run ;

Use the SUDAAN procedure, proc regress, to run multiple regression. This model will show the relationship between each unit increase in BMI category and cholesterol level.

 

Option 3. SUDAAN proc regress Procedure for Simple Linear Regression with Categorical BMI Variable and Reference Level
Statements Explanation
proc regress data=analysis_data;
subpopn eligible= 1 ;
nest sdmvstra sdmvpsu;
weight wtmec4yr;
class bmicat/nofreq;
reflevel bmicat=2 ;
model lbdhdl=bmicat;
rformat bmicat bmicat. ;
run ;

Use the SUDAAN procedure, proc regress, to run multiple regression. This model uses the normal BMI category as a reference category for cholesterol level.

 

Highlights from the output include: