M.tareq ☆ 20190721 21:09 (1851 d 17:05 ago) Posting: # 20418 Views: 3,445 

Dear all, I've a question about the different estimation methods used for deriving BE in standard crossover 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 dropout 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 dropout 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 intrasubject 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? Thanks Mahmoud 
Helmut ★★★ Vienna, Austria, 20190722 19:19 (1850 d 18:56 ago) @ M.tareq Posting: # 20426 Views: 2,679 

Dear Mahmoud, not sure whatyou mean by “MM”. Methods of Moments? If yes, it gives only initial estimates for REML in a mixedeffects model. If the study is balanced, the optimizer should converge in the first iteration. In this case REMLestimates equal MoMestimates. ❝ 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 dropout using REML the BE decision changes. When you mean that the data was incomplete (i.e., the subject dropped out but you have still data of some periods) that’s quite possible. A mixedeffects model (including all data) tries to recover all information. However, in most cases you get pretty similar results by
❝ I did some search, there's some paper indicating that the estimation using MM in unbalanced design might lead to biased variance estimates, … Which one – Patterson/Jones? ❝ … although, in our case, the number of subjects on reference product is the same as the one on test product. How is that possible when you had a dropout? I would never try a mixedeffects model on the whole data. The residual variance should be the same (unless you run into convergence issues of the optimizer). Only the betweensubject variance will be different. The latter is only “nice to know”. The EMA is clear in the BEGL: Only subjects with at least one treatment of T and R should be included. If you want to submit to the FDA, exclude the subject as well. Not informative. — Diftor heh smusma 🖖🏼 Довге життя Україна! _{} Helmut Schütz The quality of responses received is directly proportional to the quality of the question asked. 🚮 Science Quotes 