Dear all,

I would like to ask your advice regarding logistic regression.

In my study, I am looking at the effect of the introduction of a clinical algorithm on an outcome of interest. The clinical algorithm is composed of three simple rules, all of which are binary (yes/no). If any of these rules are satisfied, this warrants treatment with treatment X. If no rules are satisfied, this warrants treatment with treatment Y.

In my dataset, I have cases from the year prior to the introduction of the algorithm, and the year after the introduction of the algorithm (variable = group in this case).
  • The primary outcome is visual loss, a binary outcome (yes/no).
  • The predictors are all binary: rule1, rule2, rule3, group, treatment.
Plugging these variables into STATA for all patients, I receive the following output. Because there are 8 possible combinations of the rules, I coded this as "conditions". I added a group*treatment interaction term as well. Does my data suggest that group had a significant impact on the outcome of interest? Or was there something intrinsically wrong about combining data from both datasets? The rationale behind combining all patients, even though there was a change in clinical practice, was that the results would appear to be conditioned by the "conditions" i.e. combinations of rules satisfied by the patient on presentation. Many thanks for all your help!

Array