I would be grateful for advice on how to approach the following situation: I have a count variable X and four binary variables A, B, C, D. The count variable is the independent variable (it refers to the number of adverse experiences in childhood) and the binaries are dependent variables (they refer to certain adverse outcomes in adulthood). A respondent in the dataset can have any combination of outcomes, e.g. A, AC, BCD etc. I want to measure the strength of the association between the count variable X and the outcomes A, B, C, D conditional on the levels of the other outcomes.
I’m not sure how best to approach this. Would it be justified to reverse the role of variables and treat the count variable X as the outcome and A-D as predictors? So this would be negative binomial regression (there is overdispersion). In this way the association between X and A (B, C…) would be estimated holding other binary variables constant. But it seems to me that logically it would be dodgy as we would be predicting something that happened earlier with something that happened later.
Or should I use MANOVA instead (but I’ve read somewhere that the interpretation of results is not straightforward).
Or should I use a generalized linear mixed model (never tried it before) as suggested here https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2798811/ .