

SAS MACROS 
1. Studying missing data patterns. · The macro is designed to look at missing data in four ways: the proportion of subjects with each pattern of missing data, the number and percentage of missing data for each individual variable, the concordance of missingness in any pair of variables, and possible unit nonresponse. · The SAS macro is %missingPattern: %missingPattern(datain=, varlist=, exclude=, missPattern1=, dataout1=, missPattern2=, dataout2=, missPattern3=, dataout3=, missPattern4=, dataout4=); · Download: macro studying missing pattern.sas 2. MIANALYZE for survey weighted linear regression models from multiplyimputed data. ·
This macro is designed to use the MIANALYZE
procedure to combine the regression coefficient estimates of survey weighted
linear regression models (fitted using PROC SURVEYREG) from multiplyimputed
data. ·
The SAS macro is %MI_SREG: %MI_SREG(dset, outcome, var, catVar, strata, cluster, weight, output1, output2); ·
Download: MI_SREG.sas 3. MIANALYZE for survey weighted logistic regression models from multiplyimputed data. ·
This macro is designed to use the MIANALYZE
procedure to combine the regression coefficient estimates of survey weighted
logistic regression models (fitted using PROC SURVEYLOGISTIC) from
multiplyimputed data. ·
The SAS macro is %MI_SLOGIT: %MI_SLOGIT(dset, outcome, var, catVar, strata, cluster, weight, output1, output2); ·
Download: MI_SLOGIT.sas 4. Forward
stepwise selection for survey weighted logistic
regression models using PROC SURVEYLOGISTIC ·
The SAS macro is %SLOGIT_STEPWISE: %SLOGIT_STEPWISE(dset, outcome, force, forceCat, varlist, catVarlist, strata, cluster, weight, alpha1, alpha2, output); ·
Download: SLOGIT_STEPWISE.sas 

5.
Backward selection for survey weighted linear regression models using PROC
SURVEYREG ·
The SAS macro is %backward: %backward(dataset, forceInVar, varlist, catVarlist, outcome, weight, strata, cluster, alpha); 