proc mianalyze

[FanRu]

Hello statisticians,

I have been using proc mianalyze for sometimes and i found that some variables showed statistical significance in each dataset after multiple imputation, but they didn't show statistical significance in  proc mianalyse. I tried to change the number of imputation but unfortunately found that it did not work. I would be very grateful if anyone could explain this phenomenon.

[Ksharp]

Calling @Rick_SAS 

[FanRu]

Thanks a lot!

[Rick_SAS]

Please post your code so that we can have a better chance of answering your questions.

[FanRu]

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!

[SAS_Rob]

Without seeing your code I would say that this is likely due to a large fraction of missing information (FMI).  You should expect an increase in the variance (and thus a reduction in significance), specifically the between imputation variance, when the FMI is high.  This section of the documentation will be helpful in that regard.

SAS Help Center: Multiple Imputation Efficiency

The other possible cause is that you have a bad imputation model (in the Proc MI step) or there is non-convergence in the MI models.

SAS Help Center: Checking Convergence in MCMC

If you can post your code and LOG (including the MI, modeling and MIANALYZE steps) then there might be something more concrete I can suggest.

[FanRu]

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;