I wanted to make sure that my approach to performing a matched case-control study is on the right track. I am looking at a group of patients who all underwent the same surgical procedure for a given disease. Initially logistic regression analysis was performed which found that there was an association between disease D and death following the procedure. I then created two groups, one with disease D (D+) and another without the disease (D-). This was done using coarsened exact matching (cem) via the
MatchIt package in R. I matched the groups based on age, gender, race, income status, insurance type, and other comorbidities.
So now I have 2 groups (D+ and D-) with approximately 8000 patients total. I want to see if disease D, even after matching, is associated with post-procedure death. I have read that conditional logistic regression should be used in this case. My confusion stems from the fact that implementations in R for conditional logistic regression (e.g.
clogit from the
survival package) seem to be using cox regression (
coxph). Why is this the case? Would it be incorrect to use unconditional logistic regression (e.g.
glm) along with the weights created during matching? As an aside, why can't the chi-square test simply be used instead?