Is it common practice (and adequate) to regroup two binary dependant variables into a single 4-level dependent variable to take advantage of the multinomial regression? For instance, say we have information on two related conditions (outcomes) A and B. A new 4-category variable would be defined such that:
category 1 = Neither conditions A nor B category 2 = Condition A (only) category 3 = Condition B (only) category 4 = Both conditions A and B
This allows running a single multinomial regression instead of using two binary logistic models that include the same predictors.