I am conducting a data analysis on an Epidemiology cross-sectional study.
Suppose the outcome variable is an binary variable for health status (1=health, 0= unhealth). And the exposue is infection at early year. The hypothesis is that exposed to early infection can affect later health.
Another variable is gender which people which can affect the health status. There also a bunch of other variables that could be confounders.
The analysis we did pretty simple, we used a logistic regression analysis, we treat gender and other potential confounders as predictors and include some interactions such as gender*infection term in the model.
However, some professors (non-statisician) insisted that we should do the analysis separately, one model for male and one model for female.
I think separated model cannot even study the interactions.
My question is what are the advantages to run regression analysis separately (one for male and one for female)?