This example uses the demoadv dataset (download at Sample Code and Datasets). In this example, you will assess the association between systolic blood pressure (mean_spb) — the outcome variable — and calcium supplement use (anycalsup) — the exposure variable — after controlling for selected covariates in NHANES 20032004. These covariates include race/ethnicity (ridreth1), age (ridageyr), and body mass index (BMI) (bmxbmi).
The demoadv dataset for this example only includes those with MEC weights (wtmec2yr>0).
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 examine 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. For example, you could model 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 and eligibility variables are created:
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 (dxdtobmd^= . and ridreth1^= . and ridageyr^= . and bmxbmi^= . and anycalsup^= . ) and wtmec2yr> 0 and (ridageyr>= 20 ) then eligible= 1 ; 
eligibility 
These programs use variable formats listed in the sample program. You may need to format the variables in your dataset the same way to reproduce results presented in the tutorial.
The association between the dependent and independent variables is expressed using the model statement in the in the proc surveyreg 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 class statement. In proc surveyreg, the dependent variable is NEVER specified in a subgroup or a class statement because it must be a continuous variable.
Statements  Explanation 

proc surveyreg data =demoadv ; 
Use the proc surveymeans procedure to obtain number of observations, mean, and standard error. 
stratum sdmvstra; 
Use the stratum statement to define the strata variable (sdmvstra). 
cluster sdmvpsu; 
Use the cluster statement to define the PSU variable (sdmvpsu). 
class anycalsup; 
Use the class statement to define a dummy variable for the independent variable (anycalsup). 
model mean_sbp=anycalsup;

Use the model statement to specify the dependent variable (mean_sbp) and the independent variable (anycalsup). 
domain eligible;

Use the domain statement to specify the table layout to form the subpopulations of interest. This example uses the eligible participants for the multiple regression. 
weight wtmec2yr; 
Use the weight statement to account for the unequal probability of sampling and nonresponse. In this example, the MEC weight for 2 years of data (wtmec2yr) is used. 
format anycalsup yesnos. ; 
Formats the anycalsup variable. 
Highlights from the output include:
Statements  Explanation 

proc surveyreg data =demoadv ; 
Use the proc surveymeans procedure to obtain number of observations, mean, and standard error. 
stratum sdmvstra; 
Use the stratum statement to define the strata variable (sdmvstra). 
cluster sdmvpsu; 
Use the cluster statement to define the PSU variable (sdmvpsu). 
class riagendr anycalsup ridreth1 bmicat; 
Use the class statement to define dummy variables. 
model mean_sbp=anycalsup riagendr ridreth1 ridageyr bmicat/ solution ; 
Use the model statement to specify the dependent variable (mean_sbp) and the independent variables (anycalsup riagendr ridreth1 ridageyr bmicat). 
domain eligible; 
Use the domain statement to specify the table layout to form the subpopulations of interest. This example uses the eligible participants. 
weight wtmec2yr; 
Use the weight statement to account for the unequal probability of sampling and nonresponse. In this example, the MEC weight for 2 years of data (wtmec2yr) is used. 
format anycalsup yesnos. ; 
Formats the anycalsup variable. 
In this step, the SAS output is reviewed.