I want to determine if smoking is related to this cancer in an observational study. I have data from 1000 subjects with following variables:
age (continuous numeric) gender (male/female) income (continuous numeric) smoking (yes/no) cancer (yes/no)
However, since this is an observational study, groups may not be balanced. To determine relation between smoking and cancer while correcting for covariates (age, gender and income), I think both of following methods can be used:
1. Propensity score matching 2. Logistic regression analysis: cancer ~ smoking + age + gender + income
Are both these methods valid for such an analysis? If so, which is better and why? Or some other method is most appropriate for this? Thanks for your insight.
Edit: Cancer (outcome variable) is present in about 100 subjects while 900 subjects have no cancer. Also, I would like to have suggestions on this particular set of data rather than a general answer.