I'm trying to work out the differences between conditional logistic regression and generalized estimating equations GEEs for repeated measures binary data. GEEs seem to be far more popular and I'm not sure why.
I ran a simulation with repeated measures on the same subject where I omitted a subject-specific confounding variable from the model for both the conditional logistic regression and the GEE logistic regression. Conditional logistic regression gave me unbiased estimates of the effect of interest while GEE logistic regression yield biased (but ultimately consistent) estimates.
I'm struggling to understand why conditional logistic regression is superior to GEE for reducing this omitted variable bias.