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 * 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 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.
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. $\endgroup$ – Krantz Feb 16 '19 at 22:10A * 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. $\endgroup$ – StatsStudent Feb 16 '19 at 22:30