## REML, ML and MM different BE decision [RSABE / ABEL]

Dear Mahmoud,

not sure whatyou mean by “MM”. Methods of Moments? If yes, it gives only initial estimates for REML in a mixed-effects model. If the study is balanced, the optimizer should converge in the first iteration. In this case REML-estimates equal MoM-estimates.

» 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.

When you mean that the data was

» 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 mixed-effects model on the whole data. The residual variance should be the same (unless you run into convergence issues of the optimizer). Only the between-subject variance will be different. The latter is only “nice to know”. The EMA is clear in the BE-GL: Only subjects with

not sure whatyou mean by “MM”. Methods of Moments? If yes, it gives only initial estimates for REML in a mixed-effects model. If the study is balanced, the optimizer should converge in the first iteration. In this case REML-estimates equal MoM-estimates.

» 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.

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 mixed-effects model (including all data) tries to recover all information. However, in most cases you get pretty similar results by- Mixed-effects model of all data

- Mixed-effects model of complete data (dropout excluded)

- Fixed-effects model like #2.

» 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 mixed-effects model on the whole data. The residual variance should be the same (unless you run into convergence issues of the optimizer). Only the between-subject variance will be different. The latter is only “nice to know”. The EMA is clear in the BE-GL: 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.—

Helmut Schütz

The quality of responses received is directly proportional to the quality of the question asked. 🚮

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*Dif-tor heh smusma*🖖Helmut Schütz

The quality of responses received is directly proportional to the quality of the question asked. 🚮

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### Complete thread:

- REML, ML and MM different BE decision M.tareq 2019-07-21 19:09 [RSABE / ABEL]
- REML, ML and MM different BE decisionHelmut 2019-07-22 17:19