Growth curve model with structured missingness I am attempting to fit a growth curve to data relating to school-aged children, 11-16. The data is collected at each year, across 5 years. Some individuals will have complete data (all 5 time points), others will leave the study via attrition (move schools or parents opt out of study), and then there are some individuals that will have been in their final years at school when data collection began, so they do not have data at every time point as they leave due to their age (move onto college or into the workplace). The same is true for those that enter the school in the later waves of data collection.
How should the latter category of individuals be considered? Could the growth curve include these individuals or should they be removed? There would be a high proportion of individuals lost, maybe 20-25%? Imputation seems incorrect in this instance as they aren't missing values as such for those individuals.

I agree with your intuition that it would be incorrect to use any imputation scheme here.
The good news is that mixed models can handle this kind of structural missingness "out of the box". There is nothing special that you need to do.
