I have a question regarding an intervention design and statistical analysis related to it.

My intervention design has 2 cohorts: per-intervention cohort and the post-intervention cohort. Each cohort has around 1000 people, but around 70% people from the pre-intervention will go to the post intervention period. In other words, some people are only in the pre intervention cohort and some new people in the post-intervention cohort, and some in both periods. The outcome measure is binary, and I am going to run 2 logistic regressions and compare the changes of predictors. I am aware of the argument that comparison of logit coefficients across groups can be misleading if underlying variance heterogeneity is present (e.g., Allison 1999).

My question is: apart from variance heterogeneity, is there issue with the dependence of observations across models as some people are in both pre and post periods, and if so, how can I address it from a statistical standpoint? Any suggestions or references?

Many thanks.

James