Logistic regression on correlated data (without clusters) I would like to create logistic model using the OCA STAT Act data in R, however, since this data is a compilation of court appearances since 2020, some defendants may have appeared before court more than once, meaning the observations would not be independent. OCA does not provide data on specific individuals and I do not know how else I would cluster the data. Is there any way that I could run a logistic regression on the correlated data correctly? I was thinking about using GEE, but this requires clusters. Another thought I had was to use individual courts as clusters or to cluster defendants with the same race, ethnicity, gender, and age. Let me know what your thoughts are.

You could check out if it actually matters in this case. That means: Use subsamples of  your data splitting them in train and test data (cross-validation with several splits). If prediction results are OK for you using the test data on the models trained with the training data on all splits (typically, you would take 3-5 splits) than you are fine. For more details on cross-validation see wikipedia and for implementation in R see geeks for geeks
