topic Re: Problem with multiple imputation code in SAS Programming

Problem with multiple imputation code

amcr729 — Fri, 12 Nov 2021 15:14:49 GMT

Dear all, I have written code to conduct multiple imputation per guidance here: https://stats.idre.ucla.edu/sas/seminars/multiple-imputation-in-sas/mi_new_1/. I cannot complete the final, 3rd stage of the imputation process--i.e. pooling of estimates from imputations-- because in stage 2 I cannot successfully output the estimates to be pooled. The problematic code and resulting errors messages are in red/bold below. Please help-- thank you!!

/*1. imputation phase*/

proc mi data=siya nimpute=20 out=mi_fcs;

class share give receive agyw drugs depression

ss worksite mobility pimp homeless money unsafe

disclose vs ed civil alcohol violrec viol;

fcs plots=trace(mean std);

var share give receive agyw drugs depression

ss worksite mobility pimp homeless money unsafe

disclose estigmasw estigmahiv astigmasw

astigmahiv pills303 pillsreceive303

pillsgive303 vs ed civil alcohol violrec viol ;

fcs discrim(share give receive agyw drugs depression

ss worksite mobility pimp homeless money unsafe

disclose vs ed civil alcohol violrec viol / classeffects=include) nbiter=100;

run;

/*2. analysis */

/*model 1*/

proc genmod data=mi_fcs;

class pid civil (ref="0") depression (ref="0")

alcohol (ref="0") ed (ref="0") mobility (ref="0") unsafe (ref="0") money (ref="0") worksite (ref="0") /param=ref;

model vs=

pills303

agyw

civil

alcohol

drugs

depression

mobility

worksite

pimp

violrec

homeless

money

unsafe

estigmasw

estigmahiv

astigmasw

astigmahiv disclose/Dist=poisson Link=log;

Repeated subject = pid / type=Ind;

estimate 'pills303' pills303 1;

by _imputation_;

ods output ParameterEstimates=gm_fcs;

title 'VS on pills given and/or received with MI';

run;

Above receives message: "WARNING: Output 'ParameterEstimates' was not created. Make sure that the output object name, label, or path is spelled correctly. Also, verify that the appropriate procedure options are used to produce the requested output object. For example, verify that the NOPRINT option is not used."

Therefore I cannot complete following, phase 3, because GM_FCS does not exist.

/*3. pooling phase*/

TITLE " MULTIPLE IMPUTATION Linear REGRESSION - FCS";

PROC MIANALYZE parms(classvar=level)=gm_fcs;

class share give receive agyw drugs depression

ss worksite mobility pimp homeless money unsafe

disclose vs ed civil alcohol violrec viol;

MODELEFFECTS INTERCEPT share give receive agyw drugs depression

ss worksite mobility pimp homeless money unsafe

disclose estigmasw estigmahiv astigmasw

astigmahiv pills303 pillsreceive303

pillsgive303 vs ed civil alcohol violrec viol;/*all variables from all models*/

RUN;

Re: Problem with multiple imputation code

PaigeMiller — Fri, 12 Nov 2021 15:36:37 GMT

According to the PROC GENMOD documentation, you need the PRINTMLE option to obtain the parameter estimates when you use a REPEATED statement.

Re: Problem with multiple imputation code

amcr729 — Mon, 15 Nov 2021 20:47:51 GMT

thank you so much. That enabled me to complete stage 2 of multiple imputation, but now I cannot get through the third and final pooling stage because when I run the code (see "/*3. pooling phase*/" in original post), SAS gets hung up running and never competes the procedure. Any ideas of how to fix this? Thanks! There are just 890 observations in the dataset.

Re: Problem with multiple imputation code

Saba_Zaki — Wed, 23 Aug 2023 14:15:49 GMT

Hi team, I've used the below code for multiple imputation.

After doing imputation, I'm running multinomial logistic regression then running proc analyse.

proc mi data=data.newdata seed=876 nimpute=5 out=outfcs;

class group sex hispan;

fcs nbiter=40 logistic (group/details);

var group sysbp01 diabp01 a1c_01 chol_01 bmi_01 sex hispan;

run;

proc logistic data = outfcs; class group (ref = "3") sex (ref = "0") hispan (ref= "1") / param = ref; model group = sysbp01 diabp01 a1c_01 chol_01 bmi_01 age01 sex hispan / link = glogit covb;

by _Imputation_; ods output ParameterEstimates=lgsparms CovB=lgscovb;

run;

proc mianalyze parms (classvar=classval)=lgsparms; class sex hispan; modeleffects intercept sysbp01 diabp01 a1c_01 chol_01 bmi_01 age01 sex hispan ; run;

And getting only pooled/combined estimates. But I need relative estimates.

Thank you.