In an intervention study with subjects randomly allocated to two treatments (treat A and B) and three time points (time) plus an additional baseline measurement (dv_base), I've set up the following model to test for differences in temporal courses of treatments for the outcome (dv), thereby allowing for individual intercepts and slopes:

lmer(dv ~ dv_base+treat*time+(1+time|subject))

Fixed effects:
                    Estimate  Std. Error DF t value
(Intercept)        -1.080041   0.126665  58  -8.527
dv_base            -0.888656   0.090617  53  -9.807
treatB              0.645455   0.190541  53   3.387
time               -0.001726   0.163044  58  -0.011
treatB:time         0.377888   0.271972  58   1.389

I'm interested now in comparing estimated treatment means for let's say the last time point and I've centered 'time' accordingly. The term 'treatB' shows the difference which is relatively high and significantly different from 0 (t(53)=3.387). Now I want to compute the effect size of this difference, based on the model, not on the observed values. From my understanding I could obtain raw effect sizes by simply reporting the value of the term 'treatB' (=0.645). When it comes to standardized effect sizes (comparable to Cohen's d) I could simply take the t-value and the degrees of freedom and use the formula 2*t/sqrt(DF)=0.930. 

My question: Is this a correct procedure? I'm somewhat unsure as I've never encountered it in the literature.

And help on this is greatly appreciated.

Andrea


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