I have been trying to combine estimates for random effects after running PROC GLIMMIX in a multiply imputed dataset. However, I keep getting errors. Any help is appreciated. The code I used in GLIMMIX to create a dataset with random effects: PROC GLIMMIX DATA = FULL.DSRTLONGFULL METHOD=QUAD(FASTQUAD) noclprint plots=all; by _IMPUTATION_; CLASS RAHHIDPN STRATCLUS alc (ref='0') smoke(ref='0') GENDER (REF='2') RACE_ETH(ref='1') maritalstat(ref='1')meds(ref='0') hear(ref='5') sleep(ref='1'); MODEL COGSCR = time BMI time*BMI time BMI time*BMI PAscr SEscr alc smoke GENDER EDU ciyrsn RACE_ETH R7AGE_N maritalstat income cesd meds hear sleep numcond Funcstat/ SOLUTION DDFM=RESIDUAL obsweight=lev1wt ; RANDOM INTERCEPT /subject=STRATCLUS solution type=VC weight=lev3wt; /**LEVEL 3 VARIABLES**/ RANDOM INTERCEPT /subject=RAHHIDPN(STRATCLUS) solution type=VC weight=lev2wt; /**LEVEL 2 VARIABLES**/ COVTEST/WALD; ODS OUTPUT parameterestimates=FULL.PARAMBMI covparms=full.randombmi; store BMIall; RUN; The MIANALYZE Code: proc sort data=full.randombmi; by effect subject _imputation_; run; proc mianalyze parms (classvar=Level)=full.randombmi; by subject; modeleffects intercept; run; The dataset with the estimates:   ... View more

Hi all, I am trying to pool estimates from PROC GLIMMIX( continuous dependent variable) using PROC MIANALYZE for three-level mixed linear modeling. The dataset I am using is from a complex sample with multilevel weights. I imputed (n=15) data outside SAS and I changed that imputation variable to match with SAS default. However, I keep getting errors. It looks like it had to do with the interaction term, maybe not!! How can I fix this? I have copied the SAS codes below. I'd be thankful if I could get help with this. PROC GLIMMIX DATA = IMPOUT.DSRTLONGDELETED METHOD=QUAD(FASTQUAD) noclprint plots=all; by _mult_; CLASS RAHHIDPN STRATCLUS APOE_n (ref='33') alc (ref='0') smoke(ref='0') GENDER (REF='2') RACE_ETH(ref='0') maritalstat(ref='1') meds(ref='0') hear(ref='5') sleep(ref='1') stroke(ref='0') diab(ref='0') HTN(ref='0') heartdz(ref='0'); MODEL COGSCR = time smoke apoe_n time*smoke time*apoe_n PAscr SEscr alc smoke BMI GENDER EDU ciyrsn RACE_ETH R7AGE_N maritalstat income cesd meds hear sleep numcond stroke diab HTN heartdz Funcstat/ SOLUTION DDFM=RESIDUAL obsweight=lev1wt ; RANDOM INTERCEPT /subject=STRATCLUS type=VC weight=lev3wt; /**LEVEL 3 VARIABLES**/ RANDOM INTERCEPT /subject=RAHHIDPN(STRATCLUS) type=VC weight=lev2wt; /**LEVEL 2 VARIABLES**/ NLOPTIONS GCONV=0.00001; COVTEST/WALD; ODS OUTPUT parameterestimates=IMPOUT.PARAMSMOKEdeleted; store SMOKEdeleted; RUN; DATA PARAMSMOKEdeleted; SET IMPOUT.PARAMSMOKEdeleted; RENAME _MULT_ = _IMPUTATION_; RUN; PROC SORT DATA=PARAMSMOKEdeleted; by _IMPUTATION_ ;RUN; PROC MIANALYZE PARMS(classvar=full)=PARAMSMOKEdeleted; /**Also used classvar=level, but still get the error saying nolevel variable in the dataset**/ BY _IMPUTATION_ ; CLASS APOE_n SMOKE ALC GENDER RACE_ETH maritalstat meds hear sleep stroke diab HTN heartdz; MODELEFFECTS INTERCEPT time smoke apoe_n time*smoke time*apoe_n PAscr SEscr alc smoke BMI GENDER EDU ciyrsn RACE_ETH R7AGE_N maritalstat income cesd meds hear sleep numcond stroke diab HTN heartdz Funcstat; RUN; /**SAS LOG**/ ERROR: Within-imputation Estimate missing for effect smoke in _Imputation_= 1 in the input PARMS= data set. NOTE: The above message was for the following BY group: _IMPUTATION_=1 ERROR: Observation with _Imputation_=1 is not in the input PARMS= data set. NOTE: The SAS System stopped processing this step because of errors. NOTE: PROCEDURE MIANALYZE used (Total process time): ... View more

I am using the Health and Retirement Study (HRS) to conduct a longitudinal analysis to study the cognitive trajectories of those with dementia at my baseline. HRS is a biannual survey. My dataset is in wide form and looks like the one below (for e.g.). The variable "DGXX" represents the dementia diagnosis (1=dementia, 0=no dementia) for each respective survey year "INXX" represents the year when the survey was completed. For example. Person 706 had dementia already in 1992, 707 was only diagnosed in 1998. In my study, I want to control for the number of years the person has already lived with dementia before baseline (2004). Now, I want to create a variable that tells me when the person was diagnosed with dementia for the first time. Any help would be much appreciated. Thanks.   ID DG92 DG94 DG96 DG98 DG00 DG02 DG04 IN92 IN94 IN96 IN98  IN00 IN02 IN04 706 1 1 1 1 1 1 1 92 94 97 98 00 03 04 707 0 0 0 1 1 1 1 93 94 97 98 00 02 04 708 0 0 0 0 0 0 1 93 95 97 98 00 02 05 709 1 1 1 1 . . 1 92 94 96 98 00 02 04 710 0 0 0 0 1 1 1 92 94 96 98 00 02 04 711 0 1 0 1 1 1 1 92 94 96 98 00 02 04 712 0 0 0 0 0 1 1 92 94 96 98 00 02 04 ... View more