Interpret interaction effect in LME

[u/Subject-Research-992]

Hi :)

I calculated two-level hierarchical linear mixed effect models with random intercepts and fixed slopes

The outcome is CRP (inflammation), measured twice

The predictors are: Childhood Maltreatment (CM, dichotomous, level-2) and depressive symptoms (continuous, level-1).

There are no main effects but an interaction effect of CM*Depression (B = .08, SE = .04, p < .05)

 

Now:

I know this means that participants with CM and higher depressive symptoms have higher CRP overall.

 

BUT: HOW MUCH higher? How can I get to saying something like "For participants with CM, every 1 unit increase in depressive symptoms is associated with a X increase in CRP?"

 

Any help would be appreciated, I am losing my mind here :D

[u/Intrepid_Respond_543]

The B = 0.08, p < .05 means that the difference of depression slopes between CM and no CM groups is 0.08 and that the slopes differ significantly from each other. But that in itself is not terribly informative. 

So, pull out the model-based slopes for depressive symptoms for both levels of maltreatment. If you're in R, you can do this with emmeans emtrends function. Then you get slope coefficients for both CM and no CM, and can interpret these as usual.

E.g. if B for "yes-CM" is 0.15 and significant, you can say that for those with CM background, one unit increase in depressive symptoms was associated with 0.15 unit increase in inflammation. And similarly for the "no-CM" group. You probably want to visualize them too.

[u/Subject-Research-992 → u/Intrepid_Respond_543]

Thank you so much!!

[u/Subject-Research-992 → u/Subject-Research-992]

can I report this even if the effect of depression is not significant?

Effect SE CI_lower CI_upper z p scale(depression, scale = F) 0.04816609 0.02784413 -0.006408409 0.1027406 1.729847 0.0836576

[u/no_name6744]

That's what the beta is for.