I have a model with a binary dependent variable (DV) and 5 independent variables, all of which are matched (each person, twice).
I think since these matched INDEPENDENT variables can be considered "repeated-measures", generalized estimating equations (GEE) is the best approach here. However, I have used binary logistic regression.
Do you think this analysis is valid? Or is it just of lower power or less elegant than GEE?
I can handle a lower power or less elegance, but not invalid. By "invalid" I mean where some assumptions are not met and the test is incorrect.
My guess is that since binary logit discards the correlations between the repeated measures, it is a special case of repeated measures with zero correlation. So it might still be valid, but less useful than GEE.
Am I right?
Besides, I doubt if matching the independent variables is considered repeated measures. I am confused.