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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