# multiple logistic regressions with binary predictors vs single logistic regression with categorical predictors

I would like to know which is statistically more advisable and what are the advantages and disadvantages of each approach.

My data frame data has Y, the outcome, and A and B, the predictor variables. A and B are categorical with multiple levels each (the levels are A0, A1, A2, and A3 for A; and B0, B1, B2, and B3 for B). I want to explore the interaction A * Band calculate some epidemiological measures whose formulas are more manageable when A and 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.

• Feb 16, 2019 at 21:53
• I don't agree. My question explores a different aspect. Feb 16, 2019 at 21:56
• I'm not sure I understand how your question is any different from fitting a model with an interaction effect between A & B (possibly after collapsing categories) and fitting separate models? This question has been asked many times on CV before. Feb 16, 2019 at 22:08
• model with an interaction effect between A & B after collapsing categories is not part of my question. I am not doing that in my analysis. Feb 16, 2019 at 22:10
• But you've written "I want to explore the interaction A * B and calculate some epidemiological measures whose formulas are more manageable when A and 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 sounds like trying to chose between interaction effects and fitting separate models. If that's not what you are asking, I would suggest editing this to make your question clearer. Feb 16, 2019 at 22:30