I'm not a friend of this crippled approach. Only forgot 1/3 of your data per comparison under consideration. Practically this means to me: Don't change anything in the dataset. a three period study including two references, one from EU and another from USA, or a four period study including test and reference in fed and fasted states), the analysis for each comparison should be conducted excluding the data from the treatments that are not relevant for the comparison in question." "In studies with more than two treatment arms (e.g. To cite the EMA guideline again (we had this already occasionally discussed here, also by yourself!): If you aimed for an EMA submission you have to do some sort of that. How? By collapsing the data into a 2-way study while preserving the order of the T and R treatments are per sequence (i.e., ABC → CB, BAC → AC, CBA → CA etc etc) and run stats? Or remove the 2nd formulation data completely and run stats? So that the width of the 90% CI is attributed to intrasubject CV from both 1st formulation and reference. But I am not altering any data or randomness of the treatments (Am I?). With a three way x-over study, what if the 1st formulation fails on 90% CI due to the pool variance being inflated by the 2nd formulation? Can I file something to the agency to have them to re-consider the fact that the 1st formulation actually passes BE if the study was conducted as a two way? How? By collapsing the data into a 2-way study while preserving the order of the T and R treatments are per sequence (i.e., ABC → CB, BAC → AC, CBA → CA etc etc) and run stats? Or remove the 2nd formulation data completely and run stats? So that the width of the 90% CI is attributed to intrasubject CV from both 1st formulation and reference. Here is question though but I think I might have asked you (and others) before. I love chaos and to confuse my direct reports. You should see again different results, but in reversed order. » If you are walking the fun road already: You can feed the incomplete data to PROC MIXED and the other way ’round the complete to PHX. I used GLM in SAS and then for my own amusement I ran the data in Phoenix. Well the data I have is not complete, one subject is missing period 3 (withdrew) but I elected to keep his periods 1 and 2 data since period 2 was the reference arm. It is a 3-way 2 test vs reference study (tada! You guessed it! We have common/pool variance!) If the US RLD has a lower variance than the European reference, in the EMA’s model you will get a wider CI because the ‘dampening effect’ of the US RLD is not applicable.
#Winnonlin. full
The former comes from the full model with a pooled variance of all formulations, whereas the latter pools only variances of test and the European reference. In your example substitute SAS by FDA and WinNonlin by EMA. Is this a hypothetical example or from the ‘real world’? On the other hand different results in higher-order Xovers are possible (again independent from software). With the correct coding I never saw different results from SAS and PHX.
#Winnonlin. software
That’s independent from the software you use. Same as above, but for the EMA you have to exclude the “uninteresting” treatment, while for the FDA I guess (!) you keep all in the model and report only the relevant pair.