I am bit stuck when it comes to how best to account for repeated measures in a model with a binary outcome.
I am trying to model some data related to the processing of complaints, and whether a complaint was passed on or not. It is a model that will be used for inference on variables that may be related to whether or not a complaint gets passed on.
The outcome variable is a binary one, and is whether or not a complaint gets passed on.
Within the data are individuals who were subject to a complaint, and the dataset covers complaints received over a period of five years. Some of the individuals in the dataset are subject to two or more complaints during the period, but the vast majority are only subject to one complaint only. Where there are repeated measures, the clusters are generally very small (most individuals subject to more than one complaints were only subject to two complaints during the period, but some are subject to 10 or more, and in one case, almost 100).
So, basically there are repeated measures in the data, but not in the majority of cases.
I figured I could either:
Just include the first complaint received against any individual during the period in the data for a logistic regression model, and exclude the rest of the data.
Run a series of models that only include one randomly chosen complaint against each individual in the data for a logistic regression model, and then average the coefficients and test statistics across the models.
Find an appropriate model than accounts for repeated measures in the data, but that does not bias coefficients towards the outcome of cases for individuals that were subject to many complaints. Such individuals appear to be more likely to have a complaint against them passed on.
I'm thinking overall, the second option may be best, but was just wondering whether a better model might be possible that includes all of the data, and also whether the second option would give valid results.