I am estimating a logistic model for a binary $Y$ (0=controls, 1=cases), and a set of covariates ($x_1, x_2, x_3, \ldots$) including sex (male, female). I know from the data that risk of $y$ is quite different for males and females with certain covariates.
What I was trying to do, with no success so far, was to fit one single model for entire data to get estimates for $x_1, x_2, x_3$ for example, and gender specific OR for let's say $x_4, x_5$.
I am totally confused with terminology: I have tried nested analysis (which does not apply here because I have one observation per subject), multilevel modeling, and multinomial (0=controls, 1=male cases, 2=female cases). Multinomial seems like what I need, but in Stata that I am using,
mlogit gives me coefficients for y=1 and y=2 for all covariates, and I can not figure out how to get aggregate coefficients for all male and female cases for certain set of covariates (which are not supposed to be gender-specific). There must be some straightforward way to get OR for both from the same model, without manually calculating the coefficients and CIs.
Any suggestions would help.