can I switch one collinear predictor onto dependent variable position in a model? I have a situation where my current model looks like MODEL 1: C ~ A * B + (1|random effect, that is C is predicted by  A and B and their interaction and all 3 variables are continuous variables.
I am primarily analyzing it as a multilevel mixed model. However, I noticed that A & B when aggregated over all levels of the random effect turn out to be highly negatively correlated (about -.90), raising concerns of multicollinearity, however, the predicted correlation in the multilevel model ranges from -.60 to -.75 and when I compute a vif measure of MODEL 1, all vif indicators are bellow 2.
The estimates of model 1 show an effect of A (estimate 0.364, p<.001) but not B (0.0331, p=.4) and a significant A*B interaction: estimate 0.132, p<.001)
However, when  I test them individually e.g. (MODEL 2: C ~ A + (1|random effect or MODEL 3: C ~  B + (1|random effect both A and B appear significant and with opposite directions as expected. standardized estimate for A is 0.33571 and for B is -0.15296
This is primarily a correlational analysis, as there is no real causal relation or order between A, B and C in my case and I am interested in all 3 variables (theoretically i. Given that the correlation of A or B with C is negligible <.2, I am wondering if it would be sound to reverse the predictor of interest (A) into a DV position, so running a model like the one below
MODEL 4: A ~ C*B +  (1|random effect)
Would this be a sound way to deal with collinearity in this case, and would a result from this model allow me to more confidently accept or interpret the results from model 1, mainly the interaction and the relation between A & C without concerns for the collinear predictors in model 1?
Thanks!

A couple of points:

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*The fact that most of your diagnostics come back without issue is exactly why multicollinearity is so dangerous-- it can make your model do strange things that really can't be "caught" by standard diagnostics.


*Multiple Regression of any kind is by definition directional. E.g (a ~ b * c) is fundamentally different than (b~a * c). The just aren't the same thing and have totally different interpretations. If you care about A~ C * B, then sure, run the model. But it isn't the same as your initial analysis.
If really all you care about is the pairwise relationships, then it probably makes more sense to just do pairwise t-tests with a p-value adjustment, and maybe visualize the correlation. Without some sort of hypothesized directional relationship, linear models aren't really your friend here in my opinion.
