I have a logistic regression model that is currently of the form:
Event ~ Vacc + Age
I want to start including interaction terms for different types of vaccine. I have been able to do:
Event ~ Vacc + Age + Vacc*VaccRoute
VaccRoute = 0 for intramuscular and
= 1 for intranasal. This gives me nice results.
Now I want to look at the effect of vaccine brand. There are approximately 20 different vaccine brands in my data. I have made dummy variables for them all.
My question is, do I model this as A or as B?
A, i.e. a single model
Event ~ Vacc + Age + Vacc*Brand1 + Vacc*Brand2... + Vacc*Brand20
B, i.e. multiple runs of the model changing the interaction term each time
Event ~ Vacc + Age + Vacc*Brand1 Event ~ Vacc + Age + Vacc*Brand2 ... Event ~ Vacc + Age + Vacc*Brand20
Do I "use up" statistical power more doing it one way? Do I generate erroneous confidence intervals doing it the other?
Apologies for such a basic question but I haven't been able to find a succinct answer anywhere on CV or elsewhere.