I'm fitting a mixed model where measurements are repeated daily for each participant (for several variables). The data are in a format where each participant has a row for each day of the study.
Instead of leaving cells empty for missing data empty and keeping other data for each day, the person entering the data deleted the entire row for the day if any variable was missing. They didn't delete the entire record for the participant, they just deleted the entire day.
How will this change parameter estimates compared to a dataset where days with incomplete data were kept? basically I'd like to know more about what lmer() or lme() are doing under the hood. If I have 60 full entries for day 1-3, but only 50 full entries for day 4 (10 completely missing rows), how will that differ from a model where I have 50 full entries and 10 partial entries for day 4? I've learned a little bit about Maximum Likelihood Estimation, but not enough to have any practical sense of what is going on.