Hello everyone,

I am estimating a discontinuous growth curve model for a study about the impact of a treatment. All patients received the treatment, and had 3 pre measurements and 2 post measurements. Outcome is continuous. Model has a random intercept for the individual. The main variables in model: post (dummy for post period), time (time from start of study) and their interaction; these variables relate to treatment effect. I also have an additional demographic continuous variable, which is measured at the start of the study.

My question is actually about interpreting the demographic continuous variable called "cont". It is negative. Can I say for example: "cont is negatively associated with the outcome variable, such that patients with higher values of cont have lower values of the outcome? An increase of cont by one point leads to a 0.52 decrease in the outcome variable?"

I have shown the code below.


Code:
. mixed outcome post_dummy time time_post cont || patient:

Performing EM optimization:

Performing gradient-based optimization:

Iteration 0:   log likelihood = -509.03916  
Iteration 1:   log likelihood = -509.03916  

Computing standard errors:

Mixed-effects ML regression                     Number of obs     =        170
Group variable: patient                         Number of groups  =         34

                                                Obs per group:
                                                              min =          5
                                                              avg =        5.0
                                                              max =          5

                                                Wald chi2(4)      =     296.03
Log likelihood = -509.03916                     Prob > chi2       =     0.0000

------------------------------------------------------------------------------
     outcome |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
  post_dummy |   15.17095   1.542634     9.83   0.000     12.14744    18.19446
        time |   .1499465   .5509066     0.27   0.785    -.9298107    1.229704
   time_post |  -.5301381   .5660319    -0.94   0.349     -1.63954     .579264
        cont |    -.52041   .2454857    -2.12   0.034    -1.001553   -.0392668
       _cons |   85.29038   4.588404    18.59   0.000     76.29727    94.28348
------------------------------------------------------------------------------

------------------------------------------------------------------------------
  Random-effects Parameters  |   Estimate   Std. Err.     [95% Conf. Interval]
-----------------------------+------------------------------------------------
patient: Identity            |
                  var(_cons) |   6.663114   2.585102      3.114834    14.25344
-----------------------------+------------------------------------------------
               var(Residual) |    19.0831   2.314476      15.04568    24.20395
------------------------------------------------------------------------------
LR test vs. linear model: chibar2(01) = 16.53         Prob >= chibar2 = 0.0000

.
. margins, at(cont = (13 17 21))

Predictive margins                              Number of obs     =        170

Expression   : Linear prediction, fixed portion, predict()

1._at        : cont            =          13

2._at        : cont            =          17

3._at        : cont            =          21

------------------------------------------------------------------------------
             |            Delta-method
             |     Margin   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
         _at |
          1  |   83.23216   1.426538    58.35   0.000     80.43619    86.02812
          2  |   81.15052   .6469439   125.44   0.000     79.88253     82.4185
          3  |   79.06888   .8546855    92.51   0.000     77.39372    80.74403
------------------------------------------------------------------------------
thanks!