I would like to know which is statistically more advisable and what are the advantages and disadvantages of each approach.
My data frame
Y, the outcome, and
B, the predictor variables.
B are categorical with multiple levels each (the levels are
B). I want to explore the interaction
A * Band calculate some epidemiological measures whose formulas are more manageable when
B are binary each.
It is possible to keep a meaningful interpretation in my results if I split the data frame into several chunks and fit a logistic regression with binary predictors for each chunk of data. This has the advantage that I can easily calculate the epidemiological measures that are of interest for my analysis. However, this approach might compromise the sample size and there might be other disadvantages that I am not aware of.
Alternatively, I could use the full data frame and fit a single logistic regression with categorical predictors and do the same pairwise comparisons as above - more difficult but possible. This has the advantage of keeping a good sample size and probably other good properties that I am not aware of. But there might be some disadvantages that I might not be aware of and would like to know.
Thanks in advance for any help.