The following text explains the critical code necessary to create subsets of your data appropriately for SAS Survey procedure analyses. For examples of full SAS Survey procedure codes, please see the Logistic Regression module.

Example used throughout this task: You are interested in analyzing only 20-49 year old females who were tested for total cholesterol in a 2-year dataset.

First, you determine that you will include all MEC examined individuals in your data set.

The *ridstatr* variable on your demographic file designates interviewed
participants with a value=1, and interviewed plus examined participants with a
value = 2. Therefore, in the SAS data step, you use the *ridstatr*
variable (*ridstatr=2*)
to create a MEC-examined subset of data.

Next, in SAS Survey Procedures you specify the correct weight to be used in the
procedure by using a *weight* statement. Since you are using a single
2-year cycle, use the *wtmec2yr* variable.

If you wanted to complete an analysis of those who are greater than or
equal to age 20 and less than or equal to
age 49 years, are female, and have a valid measure for the total cholesterol
variable *lbxtc*, then you need to create a subset of data containing only those
observations. For SAS Survey procedures, there is no *subpopn*
statement. Instead, most SAS 9.2 Survey procedures use a *domain* statement for
domain analysis, also known as subgroup analysis or subpopulation analysis. In SAS 9.1 Survey Procedures,* proc surveymeans, proc
surveyreg, proc surveyfreq, and proc
surveylogistic* have different methods for selecting a subpopulation.

IMPORTANT NOTE

**You should not use a where clause or by-group
processing in order to analyze a subpopulation with SAS Survey
procedures. **

*proc surveymeans* has a *domain* statement
for domain or subpopulation analysis. Syntax details are in the SAS OnlineDoc:

http://support.sas.com/onlinedoc/913/getDoc/en/statug.hlp/surveymeans_sect6.htm

You can use the *%sregsub* macro available on the
SAS website at:

http://support.sas.com/ctx/samples/index.jsp?sid=483

A *domain* statement is being added to *proc
surveyreg* in SAS 9.2.

You can
perform a domain analysis by including your domain variable(s) in the *tables*
statement. Details are at:
http://support.sas.com/onlinedoc/913/getDoc/en/statug.hlp/surveyfreq_sect12.htm

To get an approximate domain analysis, you assign a
near zero weight to observations that do not belong to your current domain. The
reason that you cannot make the weight zero is that the procedure will exclude
any observation with zero weight. For example, if you have a domain gender=male
or female, and if you specify in a *data *step:

if gender=male then newweight=weight;

else newweight=1e-6;

you could then perform the logistic regression using the
*newweight* variable as:

weight newweight;

SAS hopes to add a *domain* statement for *proc
surveylogistic* in future releases, although
no timetable has been set.

SAS Technical Support.