Thanks a lot! ... View more

Thank you for your help! I also found that there is a large gap between the coefficient estimates and standard errors of some covariates between different datasets after imputation.Here below my code and LOG: /*MI for longitudinal data*/ proc mi data=trajectory_imputation out=imputed seed=2021 nimpute=20; var BMI pulse HDLC LDLC TC TG glucose UA; mcmc timeplot(mean(BMI) mean(pulse) mean(HDLC) mean(LDLC) mean(TC) mean(TG) mean(glucose) mean(UA)); run; WARNING: The TIMEPLOT option is ignored when ODS Graphics is enabled. NOTE: The EM algorithm (MLE) converges in 12 iterations. NOTE: The EM algorithm (posterior mode) converges in 1 iterations. /*Trajectory modeling*/ proc traj data=imputed out=of. outplot=op outstat=os outest=oe; id ID; var target_variable0-target_variable12; indep time0-time12; model cnorm; max 240; ngroups 4; order 3 5 4 5; risk age sex; tcov BMI0-BMI12 pulse0-pulse12 HDLC0-HDLC12 LDLC0-LDLC12 TC0-TC12 TG0-TG12 glucose0-glucose12 UA0-UA12; run; data ParameterEstimates; set oe;/*include PARMS,STDERR and COV*/ if _TYPE_="PARMS" or _TYPE_="STDERR"; run; /*mianalyze for one trajectory group*/ proc transpose data=ParameterEstimates out=ParameterEstimates(rename=(_NAME_=Parameter PARMS=Estimate STDERR=StdErr)); var INTERC01 LINEAR01 QUADRA01 CUBIC01 BMI001 PULSE001 HDLC001 LDLC001 TC001 TG001 GLUCOSE001 UA001; by _imputation_; id _TYPE_; run; ODS OUTPUT ParameterEstimates=RESULT; proc mianalyze parms=ParameterEstimates.; modeleffects INTERC01 LINEAR01 QUADRA01 CUBIC01 BMI001 PULSE001 HDLC001 LDLC001 TC001 TG001 GLUCOSE001 UA001; run; ... View more

I am using proc traj to build a trajectory model and explore whether the covariates affect each trajectory group. Since proc traj does not have a by statement, I put together the parameter estimates for building the model using each impuation.   trajectory_imputation(data):   ID TIME    BMI    pulse   HDLC    LDLC    TC    TG     glucose    UA A   2002    23.0    87.0      1.16       2.82     5.16   1.93    4.82        357 A   2003    22.8      .           1.18       .            .       1.80     .              . A   2004     .         86 . 0       .          2.30      .           .        4.54       359 B   2003    24.0    86 .0     1.19       2.30      5.17   1.75    4.54       358 .....   /*mi*/ proc mi data=trajectory_imputation out=imputed seed=2021 nimpute=20; var BMI pulse HDLC LDLC TC TG glucose UA; mcmc; run; /*traj*/ data ParameterEstimates; set oe; /*include PARMS STDERR COV*/ if _TYPE_="PARMS" or _TYPE_="STDERR"; run; /*mianalyze for one trajectory group*/ proc transpose data=ParameterEstimates out=ParameterEstimates (rename=(_NAME_=Parameter PARMS=Estimate STDERR=StdErr)); var INTERC01 LINEAR01 QUADRA01 CUBIC01  BMI001 PULSE001  HDLC001 LDLC001 TC001 TG001 GLUCOSE001 UA001; by _imputation_; id _TYPE_; run; ODS OUTPUT ParameterEstimates=RESULT; proc mianalyze parms=ParameterEstimates; modeleffects INTERC01 LINEAR01 QUADRA01 CUBIC01  BMI001 PULSE001  HDLC001 LDLC001 TC001 TG001 GLUCOSE001 UA001; run;   I also found that this situation may occur if the parameter estimates and standard errors of the variables between each imputation are large.Unfortunately, I  can't figure out the reason for the large difference between each imputed datasets.Thank you very much for your help!       ... View more