Oh, wow. Okay. Very good to know! I was confused because the 3-way interaction showed up on the non-multiple imputation output and on the multiple imputation output before the final pooling step.   So, since there's no 3-way interaction to report. Do people usually assume the 3-way is significant when a 2-way interaction is significant for one group and not the other? I guess this is probably a separate topic.   Anyways -- thank you for all of your help!   Best, Joe ... View more

Hi @Rick_SAS ,   Yeah, that makes sense. I was actually using that method, but for some reason everything came out fine except the 3-way interaction. The 3-way interaction values were all either missing (".") or 0. That's when I created the dummy interaction terms instead. Maybe the 3-way interaction is just too much calculations/pooling for multiple imputation?    Best, Joe ... View more

Hello @Rick_SAS,   Thank you for your reply. Much appreciated. So, I get why the intercept would change and I'd get an estimate for the new reference group I've set, but I'm not sure why I wouldn't get different estimates (the conditional effects?) for each group (comparing male vs female outputs) for all of the other estimates except the 2-way interactions and the 3-way interaction that include the group variable? Sorry if that's confusing. Still learning (grad student). When I change the reference groups in a non-multiple imputation model, I get different estimates for the intercept, the group variable, and the 2-way interactions that do not include the group variable. Here is the code I'm using just in case you can spot anything obvious I might be doing wrong.    /*FORMAT*/ proc format; value gender1f2m 1 = 'female' 2 = 'male'; run; proc format; value genderref 1 = '2female' 2 = '1male'; run;   /*EPII X GENDER X DHEAcort*/ ods select none; Proc mixed data = pandemic.auc_mi_imputed_dheacort method=reml covtest ; class gender1f2m; model covidanx_z = epii_mc gender1f2m DHEA_cortisol_winsorized_mc epii_gender epii_DHEAcort gender_DHEAcort epii_gender_DHEAcort anx_z diff_s4_wake BlackvsOtherRace1Black0Other incometoneedsw pubc_mean/ solution; format gender1f2m genderref.; by _Imputation_ ; ods output SolutionF=mixparms ; run;   /*POOLING 2 IMPUTATION ONLY*/ ods select all; proc print data=mixparms (obs=27); var _Imputation_ Effect Estimate StdErr ; title 'REG Model Coefficients and Covaraince Matrices' '(first 2 imputations)'; run;   /*POOLING*/ proc mianalyze parms=mixparms edf=234; class gender1f2m; modeleffects intercept epii_mc gender1f2m DHEA_cortisol_winsor epii_gender epii_DHEAcort gender_DHEAcort epii_gender_DHEAcort anx_z diff_s4_wake BlackvsOtherRace1Bla incometoneedsw pubc_mean; format gender1f2m genderref.; run;   Thank you very much!   Best, Joe   ... View more

Hi @WarrenKuhfeld ,   Thank you for trailblazing! I'll take a look at the links you provided and read through the documentation to help me troubleshoot.   Have a great night! ... View more

  Great catch! Now I get the error below. I think it's because of missing values? What can I do to exclude missing values?   ERROR: 114 invalid values were encountered while attempting to transform variable myvariable. NOTE: The SAS System stopped processing this step because of errors. NOTE: There were 239 observations read from the data set olddataset. WARNING: The data set WANT may be incomplete. When this step was stopped there were 0 observations and 8 variables. WARNING: Data set WANT was not replaced because this step was stopped. ... View more

Hello @WarrenKuhfeld ,   No worries at all -- I'm glad this code exists. I can't seem to figure out how to run the PROC TRANSREG "Univariate Box-Cox. Does the code below look correct? I keep getting an error.   proc transreg data=olddataset maxiter=0 nozeroconstant;      model boxcox(myvariable) = identify(z);      output out=.want;      run;   Thank you so much! ... View more

Hi @Reeza    I appreciate the help! However, I ran the code and got the error below:    ERROR: The transformation identify is not valid. NOTE: The SAS System stopped processing this step because of errors. WARNING: The data set WANT may be incomplete. When this step was stopped there were 0 observations and 0 variables. ... View more

  Man, I'm not even sure why I put those equal signs there when I've never done that before. I guess that's what happens when you think that running code on a weekend night instead of relaxing is a good idea.   Thank you so much!   Best, Joe ... View more