Confounding by indication- correct analogy?

[bob mcpop]

Imagine an observational study of N number of people on treatment X, I want to compare response to treatment X between alcohol drinkers and non-drinkers. I hypothesise that drinkers will respond less well. I know that drinkers have worse disease at baseline, so this difference needs to be adjusted. I can pretend that drinking is the "treatment" and use propensity score to match drinkers and non-drinkers for baseline disease severity.

However this analogy seems to me not completely correct, as drinkers will have been drinking long before they started treatment (c.f. in an observational study comparing treatment effect, the individual will have worse disease before they start treatment).

Therefore is PSM unsuitable, and if so what are the alternatives?

[Phil Bromiley]

You didn't get a quick answer. You'll increase your chances of a useful reply by following the FAQ on asking questions - provide Stata code in code delimiters, Stata output, and sample data using dataex.
There is a full slate of treatment models available in Stata 14. Some require selection on the observables and some don't. That is, some require a model of selection where the errors of the selection equation are uncorrelated with the errors of the outcome equation, and some don't.