I'm working with a dataset of ~100,000 individuals where ~500 (0.5%) individuals received treatment.
I have several continuous and count outcomes for all observations that I would like to compare between treated and untreated. It would be important for the analysis to match individuals on several characteristics (that could be binary, continuous or categorical).
I'm working with Stata.
I was brainstorming several possible scenarios that include:
stratified analyses of treated and untreated
treating it as case control study and attempting to match 'controls' to all my 'cases' where criteria of match allow. Then moving forward with analysis appropriate for that set up (conditional logit would work for binary outcomes.. not sure about continuous and count ones..)
Treatment-effects estimation, perhaps using propensity-score matching (not sure if and how it is possible to include categorical variables though..)
What analysis would be most appropriate for such dataset?