GLM: how to treat multiple variables that all measure a confounding aspect in a slightly different way? For a response variable $y$ and predictor $x_0$, I have data for a number of additional variables $x_n$, $n = 1, ..., 7$. I would like to control for a confounder in my GLM, let's call it "size". $x_1$, $x_2$, $x_3$ are all variables that measure "size" in a certain way, e.g. number of incidents, local volume of incidents, global volume of incidents. How do I treat these three variables, do I include them all, or just one of them? Should I include interaction terms, if so, just $x_1*x_2*x_3$, or also $x_1*x_2 + x_1*x_3 + x_2*x_3$? I am conscious not to construct an overly complicated model. Should I first check for relationships between each variable, in isolation? I.e. does $x_2$ actually increase with $x_3$.
I also have additional confounders, e.g. age, that are expected to vary with size. Does this necessitate further interaction terms between those two confounders? I am not sure if ending up with a 20-term GLM is a good plan... maybe I have misunderstood something. Thanks.

When you have highly correlated predictors (termed multicollinearity), it leads to problems in estimation and inference - though not prediction. So if your goal is estimation or inference, including all 3 predictors in the model without additional steps is probably not the best solution.
Because your situation involves three measures of the same construct (size), one simple solution to avoid the multicollinearity problem is to construct a single variable out of the three. Depending on the nature of the relationships, you may be able to simply do a PCA on the 3 measures. The first principal component would probably capture most of the variation in a single dimension that you could use in your regression.
I would advise first plotting the three pairwise relationships to examine the nature of the relationship and consider whether this single variable would be meaningful in your case. You could also consider whether the second and third principal components would be useful. Including all 3 principal components will not cause any multicollinearity problems (because they are orthogonal), but they are less likely to contain useful information.
