Mixed effect model with interaction I am trying to explore the effects of an intervention on different outcome measures over time. In this example I evaluate the differences in the log-transformed data of my response variable (named: measurement) over time (time1 [baseline], time2, time3) between groups (Group1, Group2, Group3).
This is my code:
t0<-lmer(measurement ~ Group * time  + (1|ID),gTUG)
And this is my output:

And the output from the anova(model) function:

(edited)(the total effect of the interaction is not significant; I think that it is due to lack of power and, additionally, my participants' data is quite variable and a bigger sample size could've improved the model)
(edited)Graph of the data:

How would you interpret the interaction properly? Would it be correct to say that only one level of the interaction is significant (i.e., Group2:timet2)?
(edited) Following the recommendations posted in the comments, I have checked whether adding an interaction significantly improves the model, and it does not significantly improve it. Output:

Therefore, would it be correct to focus on the differences between groups and across time points and report those?
I'd really appreciate your help!
Many thanks,
Anna

The effect estimators of interactions depend on the coding/contrasts. They are hard to interpret in the sense that they always compare a specific value to a baseline, interpretation depends on the baseline, and the contrast setting defines what it is. Probably from the code that you posted one could guess what the baseline is, but I'm frankly too lazy to look around and find it out (also it may depend on things that you have done before running the code that is posted above).
In any case what I recommend is this:

*

*Test for the presence of the Group:timet interaction in total by fitting a model with it and another model without it and using the anova function to compare them.


*Produce an interaction plot so that you can see what the most striking things are that cause the interaction as a whole to be significant (in case it is - I'd expect so based on the shown output, if maybe with p close to 0.05). Then interpret the plot.
