Well, there is a "brute force" method. After running PROC SURVEYFREQ with all of the DEPLVL's included, use a WHERE= option on the DATA= part of the SURVEYFREQ statement.
You would then have 5 calls to SURVEYFREQ. The first would use this:
proc surveyfreq data=yourdata;
/*remaining code to get what you already have*/
run;
proc surveyfreq data=yourdata(where=(deplvl in (0,1));
/*same code as before */
run;
proc surveyfreq data=yourdata(where=(deplvl in (0,2));
/*same code as before */
run;
proc surveyfreq data=yourdata(where=(deplvl in (0,3));
/*same code as before */
run;
proc surveyfreq data=yourdata(where=(deplvl in (2,3));
/*same code as before */
run;
The biggest caveat here is that the weighting will not be the same as for the overall test (I said it was a brute force method, not an elegant one). The second biggest is that you would likely have to run the resulting p values through PROC MULTTEST to get multiplicity adjusted p values.
But there might be another way. Have you considered PROC SURVEYLOGISTIC? You could model all the data, then extract comparisons of interest using a variety of tools (LSMEANS, LSMESTIMATE, SLICE) which enable specific pairwise comparisons and adjust using an ADJUST= option. For SAS/STAT 15.2 Example 119.2 The Medical Expenditure Panel Survey (MEPS) might be an excellent starting point, given what I can make of your survey design (not really my field).
SteveDenham
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