Hello,
I'm running into an issue working with multiply imputed data and PROC GLIMMIX. Some background information, this is a large dataset with ~2.6 million observations, with about 200k missing some basic demographic information which I imputed (race, sex, insurance status).
This is a multilevel design with patients nested inside of hospitals. The outcome is binary with categorical predictors. Race has 6 levels, and region has 4.
*MULTIPLE IMPUTATION PHASE*;
proc mi data= a.methadone_epi_11_20_19 nimpute=0 ;
var race sex insurance region hospital_number age_day_admit ethnicity;
ods select misspattern;
run;
proc mi data= a.methadone_epi_11_20_19 nimpute=40 out=mi_fcs ;
class race sex insurance;
var race sex insurance region hospital_number ethnicity ;
fcs logistic(race= region hospital_number ethnicity/link=glogit) ;
fcs logistic(sex = region hospital_number ethnicity /link=glogit) ;
fcs logistic(insurance = region hospital_number ethnicity /link=glogit);
run;
*MODEL TO INVESTIGATE EFFECT OF HOSPITAL ON GETTING AN OPIOID;
PROC GLIMMIX DATA=mi_fcs ;
by _imputation_;
CLASS hospital_number;
MODEL opioid (EVENT='1')=/cl dist=binary link=logit ddfm=bw solution oddsratio;
RANDOM INTERCEPT / subject=hospital_number type=VC solution CL;
COVTEST /WALD;
ods output parameterestimates=parms1 solutionr=rand_parms1;
run;
proc sort data=parms1;
by _imputation_;
run;
proc mianalyze parms=parms1;
modeleffects intercept ;
run;
proc sort data=rand_parms1;
by subject _imputation_;
run;
proc mianalyze parms=rand_parms1(rename=(stderrpred=stderr));
modeleffects intercept;
by subject ;
run;
*MODEL 2 ADDING IN PATIENT LEVEL DEMOGRAPHICS;
proc glimmix data = mi_fcs;
by _imputation_;
class hospital_number insurance (ref = '0') sex ethnicity (ref = '2') race (ref = '1');
model opioid (event = '1') = sex race ethnicity insurance / CL dist=bianary link=logit solution ODDSRATIO;
random intercept / subject=hospital_number solution CL ;
COVTEST / WALD;
ods output parameterestimates=parms2 solutionr=rand_parms2;
run;
proc sort data=parms2;
by _imputation_;
run;
proc mianalyze parms=parms2;
class sex race ethnicity insurance;
modeleffects intercept sex race ethnicity insurance;
run;
proc sort data=rand_parms2;
by subject _imputation_;
run;
proc mianalyze parms=rand_parms2(rename=(stderrpred=stderr));
modeleffects intercept;
by subject ;
run;
*MODEL 3 ADDING IN HOSPITAL REGION;
proc glimmix data = mi_fcs;
by _imputation_;
class hospital_number insurance (ref = '0') sex region (ref = '3') ethnicity (ref = '2') race (ref = '1');
model opioid (event = '1') = sex race ethnicity insurance region / CL dist=binary link=logit solution ODDSRATIO ;
random intercept / subject=hospital_number type=VC solution CL ;
COVTEST / WALD;
ods output parameterestimates=parms3 solutionr=rand_parms3;
run;
data parms3b;
set parms3;
if _Imputation_ = 1 then delete;
run;
proc sort data=parms3;
by _imputation_;
run;
proc mianalyze parms (classvar=full) =parms3;
class sex race ethnicity insurance region;
modeleffects intercept sex race ethnicity insurance region;
run;
proc sort data=rand_parms3;
by subject _imputation_;
run;
proc mianalyze parms=rand_parms3(rename=(stderrpred=stderr));
modeleffects intercept;
by subject ;
run;
I'm relatively new to using SAS, but everything seems to be working up to the third model where I add in the variable "region." The model runs, however it fails to converge on imputation #4, #10, #19, #21, and #25.
When I try to combine the parameter estimates following the model, I get the below message.
600 proc mianalyze parms (classvar=full) =parms3;
601 class sex race ethnicity insurance region;
602 modeleffects intercept sex race ethnicity insurance region;
603 run;
ERROR: Within-imputation observations not found for _Imputation_= 4 in the input PARMS= data set.
I'm unfamiliar with how to go about troubleshooting this, is it best to tackle the convergence issue or is there a method for dropping the imputations that did not converge from the parameter estimates that are being fed into the mi analyze procedure?
Let me know if there's additional information that I should include in this post.
Thank you!
