Is a mixed-model really required? I've got data from a retrospective cohort analysis. There are two groups that differ by a certain baseline characteristic, who were all exposed to an event (surgical procedures). The outcome of interest is a continuous non-normally distributed variable (a medical index). In both groups, about 5% of the outcome variables arise from a single individual (who underwent multiple procedures, and so has multiple outcomes recorded). The two groups are completely independent. The individual undergoing multiple procedures is different between the groups.
Obviously, there are some repeated measures introducing a potential for bias which should be recognized. However given the proportion is so small and the majority of the outcomes are independent, does this truly require a mixed-model to analyze for a difference between the group or will a simple non-parametric test suffice with acknowledement of this limitation?

Depending on your exact research question and the reasons for multiple procedures, consider removing the individuals with multiple procedures from the dataset, or only include their first or a randomly selected procedure.  You don't lose much data that way.
Keeping all of their observations as if they were independent would be anti-conservative in terms of precision, and would maybe bias the answers toward unusual individuals.  Mixed models bring their own complications, and I wouldn't be confident about estimating one if only one or two units has repeated measures (though I'm not completely sure of the implications of this).
