Hello, How to apply stepwise selection with proc hpgenselect to matched data? Thanks proc hpgenselect data=tab; class paired var1 var2 var3 var4 ; model wbc= var1 var2 var3 var4/ dist = negbin link=log ; /*random intercept / subject=paired;*/ selection method=stepwise details=all; run; ... View more

Hello,   I used the codes at below to estimate the propensity score and logistic regression for inverse probability weighting. How can I test the balance of the standardised mean differences before and after adjustment? How to obtain the synthetic n values derived from weights?   Thanks   /***CREATING PROPENSITY SCORES********/ proc sort data=tab_imput; by _imputation_;run; proc logistic data=tab_imput desc; class var1 var2 var3 var4 var5 var6 var7 var8 var9 ; model mut= var var1 var2 var3 var4 var5 var6 var7 var8 var9/link=logit rsquare ; output out=denom p=d; by _imputation_; run; proc logistic data=tab_imput desc; model mut=; output out=num p=n; by _imputation_; run; proc sort data=tab_imput ; by anonymat;run; proc sort data=denom; by anonymat;run; proc sort data=num; by anonymat;run; data tab_imput_pscore; merge tab_imput denom num; by anonymat; if mut=1 then uw=1/d; else if mut=0 then uw=1/(1-d); if mut=1 then sw=n/d; else if mut=0 then sw=(1-n)/(1-d); run; proc sort data=tab_imput_pscore; by _imputation_;run; /***PROPENSITY SCORE WEIGHTED OUTCOME MODEL****/ ods graphics on; proc logistic data=tab_imput_pscore desc; class mut(ref='no') / param=reference ; model vif (event='no') = mut/ rsquare clodds=wald lackfit ; weight sw ; by _imputation_; oddsratio mut; ods output parameterEstimates = ipw_mut ; run; ods graphics off; proc mianalyze parms=ipw_mut ; modeleffects mut; ods output parameterEstimates = ipw_mut1; run; data ipw_mut2; set ipw_mut1; OR_est=EXP(ESTIMATE); LCI_OR=OR_est*EXP(-1.96*STDERR); UCI_OR=OR_est*EXP(+1.96*STDERR); run; proc print data=ipw_mut2; var Parm OR_est LCI_OR UCI_OR Probt ; run; ... View more

Hello, Is it possible to perform the Paired Wilcoxon test and the McNemar using the Paired variable in the dataset below? Or do I have to transform the data to use proc univariate (diff)? Thanks data match; input Id $ case_control $ match_caseid $ paired age gender $ weight ethnic $; datalines; A1 Case A1 1 20 F 56 Balck A2 Control A1 1 23 F 60 Black A3 Control A1 1 22 F 65 Black B1 Case B1 2 30 M 70 Asian B2 Control B1 2 35 M 47 Asian B3 Control B1 2 33 M 75 Unknown C1 Case C1 3 40 M 80 White C2 Control C1 3 38 M 73 White D1 Case D1 4 47 M 50 Other D2 Control D1 4 41 M 58 Other D3 Control D1 4 50 M 49 Unknown D4 Control D1 4 63 M 77 Other run; ... View more