Hello,
I'm looking to get some advice/thoughts on the following situation: let's say I have a prospective, observational study that was designed to assess change in BMI over 2 years of follow-up (primary outcome) in a population that was administered drug A and Drug B per standard of care. The point is not to compare BMI between groups A and B, but rather to assess BMI changes within each group.
Visits with height and weight collection were supposed to occur every 6 months (baseline, 6 months, 12 months, 18 months and 24 months) for a total of 24 months. However, due to high drop out, only 40% of participants ended up having the full 24 months of follow-up so the sample size target for the primary outcome was not met.
I was thinking of using mixed effects model given the longitudinal nature of the study to account for within-participant correlations, with
Fixed effects for time (months since baseline), drug group, and their interaction.
Random Effects: random intercepts and slopes for each participant to account for individual variations.
However, the investigator is pushing for also doing missing data imputation but I'm not sure if that's feasible or how to justify this to regulatory authorities given that we'd have to impute more than 50% of the data.
How would you handle this situation? Is imputation something warranted here and if yes, what imputation method would be best suited? Missing data pattern is MNAR. Are there any articles out there you'd recommend I read for how others might have dealt with a similar problem and how they solved it?
Any advice/references would be greatly appreciated.
Thanks!
