I'm doing the analysis for a study where we need to assess the effect of surgery A (hand surgery) on condition B (trigger digit), adjusting for confounders via logistic regression.
The problem is that we have data from two data sets:
Set1: All patients performed surgery A and we saw whether they developed condition B.
Set2: All patients have condition B and we reported whether they performed surgery A.
By using each data set separately it's not possible to determine the effect of A on B, isn't it? because in one set all patients has the condition and in the other every one has the surgery.
I was wondering, is it correct to join the two sets? That way we would have a typical case-control situation on which to apply the logistic regression B ~ A + confounders.
Any suggestion?
UPDATE: It has been noticed that putting together the two sets wont help much, since we have no cases for the A-B- condition and therefore the OR will be an artificial 0. How can we solve this. I also thought about something more theoretical. Since we miss the A-B- we could just recruit new patients that fill that position. But this would mean to artificially pump the OR, since it's proportional to the A-B- number. How can new patients be selected in order to maintain external validity?