# 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!