I have a data set that includes a primary dichotomous independent variable (e.g., smoking), a primary dichotomous dependent variable (e.g., chronic back pain), and several covariates (e.g., diagnosis of several mental disorders, age, sex). I have calculated propensity scores for smoking using the covariates of interest. As an output, I am given a data file that includes only individuals who have been matched. Lets say I have 1000 smokers and 1000 non-smokers, with both groups matched on the covariates. Note that by "matched" I mean the two samples share similar means on the covariates, not that smokers are matched with non-smokers with similar propensity scores.
I am interested in seeing if individuals who smoke are more likely to have chronic pain in this sample matched on the covariates of interest. To do this, I would typically cross tabulate smoking/non-smoking and pain/no pain and calculate odds ratios.
Is it still appropriate to calculate odds ratios after a sample is matched on propensity scores based on covariates?
Does changing the number of participants in the control group create any problems? For instance, I could perhaps have 1000 smokers and 4000 non-smokers and calculate odds ratios using these numbers.
My intuition tells me that doing this is fine, but I want to make sure since I have never seen it done.