I have a question about adjusted odds ratios for matched data. Let's assume that we have a binary variable that we want to determine the odds ratios for (let's say smoking or not smoking, this is the exposure variable), and we also want to control for gender and weight. We have previously done matching on the other two variables, so the gender is the same within our strata and the weight is reasonably close (but not matched exactly).
Then we have a model in the form logit(P) = alpha + beta_1 * SMK + beta_2 * GEND + beta_3 * WEIGHT.
My question is, even though there is still some small variance in WEIGHT in our data, is the adjusted odds ratio for smoking status still simply exp(beta_1)?
And just to clarify, does the expression "adjusted for weight" simply mean that we have included the variable weight in our model?