Your outcome does not appear to be a rare event. You could explain this to the reviewer with the appropriate citations (ie King and Zeng, 2001 - "We study rare events data, binary dependent variables with dozens to thousands of times fewer ones").

However, you could also use the Stata implementation of the rare events logit estimator with Clarify in Stata or with Zelig in R. Stata will let you quickly estimate a rare events model with correlations in your clusters. The clustering option does not exist in R, but you correct the standard errors to correct for the correlations. Regardless, I would bet your results will hold in this framework, even if it is not a multilevel model. You could tell the reviewer that this should not be in the paper since it isn't a rare event, but that the results are robust and include the aforementioned models.

As mentioned, the Poisson does not make sense your outcome is dichotomous and not a count.

Your outcome does not appear to be a rare event. You could explain this to the reviewer with the appropriate citations (ie King and Zeng, 2001 - "We study rare events data, binary dependent variables with dozens to thousands of times fewer ones").

However, you could also use the Stata implementation of the rare events logit estimator with Clarify in Stata or with Zelig in R. Stata (Clarify) will let you quickly estimate a rare events model with correlations in your clusters. The clustering option does not exist in R (Zelig), but you correct the standard errors to correct for the correlations. Regardless, I would bet your results will hold in this framework, even if it is not a multilevel model. You could tell the reviewer that this should not be in the paper since it isn't a rare event, but that the results are robust and include the aforementioned models.

As mentioned, the Poisson does not make sense your outcome is dichotomous and not a count.