Hello, I am looking to get exponentiated coefficients after estimating a multilevel, mixed effects logistic regression with multiply imputed data.

My code is as follows:

Code:
mi estimate (_b[MED14_0]) ///
            (_b[MED14_0] + _b[2.WAVE#c.MED14_0]) ///
            (_b[MED14_0] + _b[3.WAVE#c.MED14_0]) /// 
            (_b[MED14_0] + _b[c.MED14_0#1.SEX]) ///
            (_b[MED14_0] + _b[2.WAVE#c.MED14_0] + _b[2.WAVE#c.MED14_0#1.SEX]) ///
            (_b[MED14_0] + _b[3.WAVE#c.MED14_0] + _b[3.WAVE#c.MED14_0#1.SEX]) ///
            , eform cmdok: ///
    melogit BINGE_ i.BINGE_base c.MED14_0##i.WAVE##i.SEX ///main terms + interaction
    DEP_0 ADHD_0 DB_0 i.FAMHIST_ALC i.FAMHIST_DRUG i.MJ_0 i.NIC_0 ///
        i.DRUGS_0 AGE_0 i.ETHNIC i.LUNCH ///covariates
    if MISSING_OUT==0 || SCHOOL: || SID: //extra stuff
The output I get for this is:

Code:
Multiple-imputation estimates                   Imputations       =         10
Mixed-effects logistic regression               Number of obs     =      4,218
                                                Average RVI       =     0.0507
                                                Largest FMI       =     0.1528
                                                DF:     min       =     407.24
                                                        avg       = 282,605.14
DF adjustment:   Large sample                           max       = 6308629.07
                                                F(  27,      .)   =          .
Within VCE type:          OIM                   Prob > F          =          .

---------------------------------------------------------------------------------------
               BINGE_ |     exp(b)   Std. Err.      t    P>|t|     [95% Conf. Interval]
----------------------+----------------------------------------------------------------
         1.BINGE_base |   9.493373   1.882706    11.35   0.000     6.435928    14.00329
              MED14_0 |   1.101308    .046136     2.30   0.021     1.014443    1.195611
                      |
                 WAVE |
                   2  |   1.253049   .3675333     0.77   0.442     .7045933    2.228425
                   3  |   2.643886   .7371351     3.49   0.000     1.530396    4.567533
                      |
       WAVE#c.MED14_0 |
                   2  |   1.003756   .0513321     0.07   0.942     .9077536    1.109912
                   3  |    .923678   .0449719    -1.63   0.103       .83954    1.016248
                      |
                  SEX |
                Male  |   2.146329   .7539484     2.17   0.030     1.077666    4.274726
                      |
        SEX#c.MED14_0 |
                Male  |   .8877788    .054471    -1.94   0.053     .7870215    1.001435
                      |
             WAVE#SEX |
              2#Male  |   .8968825   .3610686    -0.27   0.787     .4073536    1.974693
              3#Male  |   .4687726   .1874344    -1.89   0.058     .2140023    1.026848
                      |
   WAVE#SEX#c.MED14_0 |
              2#Male  |   1.033528   .0720354     0.47   0.636     .9014825    1.184915
              3#Male  |   1.155732   .0809422     2.07   0.039     1.007326    1.326003
                      |
                DEP_0 |   1.002952   .0061672     0.48   0.632     .9909373    1.015113
               ADHD_0 |   1.235144   .1692749     1.54   0.123     .9441368    1.615847
                 DB_0 |   .9805965   .0124518    -1.54   0.123     .9564893    1.005311
                      |
          FAMHIST_ALC |
                 Yes  |   1.284036   .2242157     1.43   0.153      .911536     1.80876
                      |
         FAMHIST_DRUG |
                 Yes  |   1.117512   .2199697     0.56   0.573     .7593975    1.644504
                      |
                 MJ_0 |
                 Yes  |   1.700471   .3236107     2.79   0.005     1.171062    2.469213
                      |
                NIC_0 |
                 Yes  |    1.66683   .3154218     2.70   0.007     1.150307    2.415286
                      |
              DRUGS_0 |
                 Yes  |   1.532934   .3410133     1.92   0.055     .9912093    2.370726
                AGE_0 |   .9446203   .1776188    -0.30   0.762     .6529157     1.36665
                      |
               ETHNIC |
               Black  |   .5275365   .2357775    -1.43   0.152     .2196909    1.266756
     Hispanic/Latino  |   .9414317   .2117789    -0.27   0.788      .605732    1.463178
               Asian  |    .471768    .150416    -2.36   0.018     .2525062    .8814238
               Other  |   .8965573   .2321105    -0.42   0.673     .5397553    1.489221
                      |
                LUNCH |
                   1  |   .9288689   .2640768    -0.26   0.795     .5316108    1.622987
                   2  |   .7690034    .137333    -1.47   0.142     .5416204    1.091846
                      |
                _cons |   .0751932   .2331758    -0.83   0.404     .0001705    33.16745
----------------------+----------------------------------------------------------------
    var(_cons[SCHOOL])|   .0116671   .0295897                      .0000809    1.681846
var(_cons[SCHOOL>SID])|   3.039094   .3954284                         2.355    3.921907
---------------------------------------------------------------------------------------
Note: Estimates are transformed only in the first equation.

Transformations                                 Average RVI       =     0.1182
                                                Largest FMI       =     0.1536
DF adjustment:   Large sample                   DF:     min       =     403.10
                                                        avg       =   1,520.15
Within VCE type:          OIM                           max       =   5,199.22

        _mi_1: _b[MED14_0]
        _mi_2: _b[MED14_0] + _b[2.WAVE#c.MED14_0]
        _mi_3: _b[MED14_0] + _b[3.WAVE#c.MED14_0]
        _mi_4: _b[MED14_0] + _b[c.MED14_0#1.SEX]
        _mi_5: _b[MED14_0] + _b[2.WAVE#c.MED14_0] + _b[2.WAVE#c.MED14_0#1.SEX]
        _mi_6: _b[MED14_0] + _b[3.WAVE#c.MED14_0] + _b[3.WAVE#c.MED14_0#1.SEX]

------------------------------------------------------------------------------
      BINGE_ |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
       _mi_1 |   .0964984    .041892     2.30   0.021     .0143394    .1786573
       _mi_2 |   .1002475   .0425527     2.36   0.019     .0165946    .1839004
       _mi_3 |   .0171066   .0389594     0.44   0.661    -.0592702    .0934835
       _mi_4 |  -.0225343   .0448122    -0.50   0.615    -.1106248    .0655562
       _mi_5 |   .1332256    .067133     1.98   0.048     .0013654    .2650859
       _mi_6 |   .1618407   .0664786     2.43   0.015     .0312957    .2923857
------------------------------------------------------------------------------
Note: Number of groups varies among imputations.
Note: Number of observations per group varies among imputations.
In the final table displayed, the coefficients are not exponentiated. Is there any way to display these as odds ratios, instead of log odds ratios? I include the eform option in my mi estimate command, but it appears to only apply to the main regression results, not the linear combinations that I generate. Thanks in advance for any help that can be provided.