REML, ML and MM different BE decision [RSABE / ABEL]
I've a question about the different estimation methods used for deriving BE in standard cross-over design.
Usually the BE is analysed using MM (Proc GLM in SAS) as per EMA (all factors as fixed effects) Q&A or REML as mentioned in FDA guidance.
Although when running a BE study and analyzing the data using MM/REML I get similar CI -we have a one drop out- ran the analysis with and without the subject and BE decision still the same. When using MM while including the drop-out using REML the BE decision changes.
the PK parameter of interest is the Cmax and it's outside the AL by 0.4
although when analyzing the data using ML, with and without the drop-out the BE is met in either cases.
I did some search, there's some paper indicating that the estimation using MM in unbalanced design might lead to biased variance estimates, although, in our case, the number of subjects on reference product is the same as the one on test product.
The drug is known to have low intra-subject variability (22%)
What are the assumption to use each method and when to do so ?
any insights on these discrepancies between the methods ? violation of assumptions?