Dear Statalist,

I am using -mixed- in Stata 15.1 to analyze a crossover trial.

In this trial participants received 3 different treatments (0,1,2), after which blood glucose concentrations were measured every 15 mins for a total of 150 minutes.

I am using the following code to analyze the data:

Code:
mixed glucose ib0.treatment##c.timepoint || pid:, covariance (id) || visit:timepoint, covariance (id)
This produces the following output:

Code:
Performing EM optimization: 

Performing gradient-based optimization: 

Iteration 0:   log likelihood = -423.54287  
Iteration 1:   log likelihood = -423.45032  
Iteration 2:   log likelihood = -423.44899  
Iteration 3:   log likelihood =  -423.4489  
Iteration 4:   log likelihood =  -423.4489  

Computing standard errors:

Mixed-effects ML regression                     Number of obs     =        330

-------------------------------------------------------------
                |     No. of       Observations per Group
 Group Variable |     Groups    Minimum    Average    Maximum
----------------+--------------------------------------------
            pid |         11         30       30.0         30
          visit |         33         10       10.0         10
-------------------------------------------------------------

                                                Wald chi2(5)      =      30.02
Log likelihood =  -423.4489                     Prob > chi2       =     0.0000

---------------------------------------------------------------------------------------
              glucose |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
----------------------+----------------------------------------------------------------
            treatment |
                   1  |   .0339427    .233476     0.15   0.884    -.4236618    .4915472
                   2  |   .5280048    .233476     2.26   0.024     .0704003    .9856093
                      |
            timepoint |   .0112455   .0024101     4.67   0.000     .0065217    .0159693
                      |
treatment#c.timepoint |
                   1  |  -.0054439   .0034084    -1.60   0.110    -.0121244    .0012365
                   2  |  -.0107255   .0034084    -3.15   0.002    -.0174059   -.0040451
                      |
                _cons |  -.2608308   .1650924    -1.58   0.114    -.5844061    .0627444
---------------------------------------------------------------------------------------

------------------------------------------------------------------------------
  Random-effects Parameters  |   Estimate   Std. Err.     [95% Conf. Interval]
-----------------------------+------------------------------------------------
pid: Identity                |
                  var(_cons) |   1.58e-12          .             .           .
-----------------------------+------------------------------------------------
visit: Identity              |
         var(timepo~t _cons) |    .000029   9.03e-06      .0000158    .0000534
-----------------------------+------------------------------------------------
               var(Residual) |   .6512137   .0534399      .5544625    .7648475
------------------------------------------------------------------------------
LR test vs. linear model: chi2(2) = 54.95                 Prob > chi2 = 0.0000

Note: LR test is conservative and provided only for reference.

My understanding of the output is that the change in glucose over time for treatments 1 and 2 are significantly different from that of treatment 0 (this is based on the coefficients of the interaction terms).

My questions are as follows:

1.) what do the coefficients for treatment 1 (.0339427) and treatment 2 (5280048) and their associated P values represent? Do they represent differences between treatments at baseline (i.e. timepoint = 0)?

2.) to identify specifically which timepoints are significantly different between treatments, I can use the marginal command as follows?

Code:
margins r.treatment, at(timepoint=(15 30 45 60 70 90 105 120 135 150)) asbalanced
Thanks in advance for your help