Here's the code's run, And using PROC MIANALYZE don't get the p value, and the SD is larger than any individual outcome
proc MI data = ITT1 seed = 128 nimpute = 10 out = aftermi;
class score90day IVT group;
fcs logistic(score90day = age baseline_covriate onset_to_randomization group score30day);
var score90day group age baseline_covriate onset_to_randomization score30day;
run;
proc print data = aftermi;
run;
data aftermi;
set aftermi;
if score90day = 0 then score90day_0to1 = 1;
else if score90day = 1 then score90day_0to1 = 1;
else score90day_0to1 = 0;
run;
/*modified Poisson Regression*/
proc genmod data = aftermi;
by _Imputation_;
class enroll_order group(desc) score90day_0to1(desc) IVT/PARAM = GLM;
model score90day = group onset_to_randomization baseline_covriate age/ dist = poisson link = log covb corrb;
ods output GEEEmpPEst=gmparms parminfo=gmpinfo Covb=gmcovb;
repeated subject = enroll_order/type = un modelse;
estimate "beta" group 1 -1/exp;
ods output Estimates=est_ds(where=(index(label,'Exp')=1));
lsmeans group/diff exp cl;
run;
proc sort data=est_ds;
by label _imputation_;
run;
proc mianalyze data=est_ds;
by label;
modeleffects LBetaEstimate;
stderr stderr;
run;
And I get the result like this for each generated dataset
This is the estimated result
when using the PROC MIANALYZE for LbetaEstimate;
I found that the Std Error seems to be larger than any Std Error in each dataset of the 10 generated. And I couldn't get the P value, can any Proffessor help me ?
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