Hi, My data is longitudinal with repeated measures of the exposure and the outcome across time. My exposure variable and outcome variable are continuous, but I'm controlling for continuous and categorical variables. I have imputed the missing covariates and used a mixed model with a random intercept. When I try using MIANALYZE, SAS does not produce combined tests/p-values for my categorical variables even though I've included the classvar=full option as described in the documentation. I get this warning message: WARNING: The within-imputation covariance matrix is singular. The total covariance matrix and related statistics in multivariate inference will be set to missing. I also tried using the TEST statement to produce a combined test of my categorical variable but I get an error message saying TEST cannot be used with class variables. Here is the structure of my code: PROC MI DATA=data OUT=outmi; CLASS group; FCS DISCRIM(group) REG(exposure); VAR group <and multiple other variables>; RUN; PROC MIXED DATA=data covtest; BY _IMPUTATION_; CLASS id group; MODEL outcome=time exposure group time*exposure /ddfm=bw solution CL covb; RANDOM intercept / SUBJECT=id TYPE=UN; ods output SolutionF=mixparms CovB=mixcovb; RUN; PROC MIANALYZE PARMS(CLASSVAR=full)=mixparms COVB(EFFECTVAR=rowcol)=mixcovb; CLASS group; MODELEFFECTS intercept time exposure group time*exposure; RUN; QUIT; I would greatly appreciate your help. I've been looking everywhere. Others have had a similar issue, but the solution seems to have been the CLASSVAR=full option which didn't work for me. Thanks
... View more