I have a binary logistic regression with Y (a disease) and 5 independent variables (and some of their 2-sided interactions which did not cause multicollinearity). All of my single IVs significantly predict Y:
- A: positive beta for males (males are more likely to get affected)
- B: a positive beta (older people are more likely to be affected)
- C (yes/no): a positive beta for smoking (smokers are more likely to be affected)
- D (continuous): a positive beta (more traumatic patients are more likely to have disease)
- E (yes/no): a Negative beta for treatment (treated cases were less likely to have diseases).
Now 4 interactions are significant and I want to interpret them. I know I should state that in a significant interaction, I should say that the effect of variable A on Y differed for B(1) and B(2). For example the effect of age on disease differed in males and females. But I don't know in which class (males or females), it was greater, and I don't know how to determine it.
The significant interactions and their direction of betas are as follows:
- 4 by 3: positive beta
- 4 by 5: positive beta
- 1 by 2: Negative beta
- 3 by 2: Negative beta
I would appreciate if you could kindly guide me. I searched for this issue but the discussions on the web are all sophisticated (e.g. this one) and beyond me. I just want to know is there a simple rule to determine the direction of interaction [i.e., "is A's effect on Y greater in B(1) or B(2)?"], given the directions of the coefficients of the involved variables (A and B) and the coefficient of the interaction itself (A*B)? (B(1) and B(2) are the levels of binary variables (man or woman) or extreme ends in continuous variables ([young and old], [easy or difficult])
Many thanks in advance.