EEU-rules, TSD-methods (lengthy question) [Two-Stage / GS Designs]

posted by Astea – Russia, 2019-09-14 14:56 (816 d 02:45 ago) – Posting: # 20597
Views: 6,442

Dear all!

Elena777, I guess I misunderstood something. Did you mean aposteriory power or interim power? If interim is 30% go to the next step by the decision tree.

» It is difficult to predict how regulators of the EEU interpret their own guideline
So true! "Words are chameleons, which reflect the color of their environment" (Learned Hand)

Helmut, what was the final conclusions on the post?

Are there any suggestions on how to deal with two metrics in adaptive trials?
For example: a). Let us consider Type II design: first step - estimated power is less than target (80%) for Cmax and more than target for AUC, besides 90%CI for AUC is OK.
1). We calculate 100(1-2αadj) CI for Cmax, should we also do it for AUC? It can fail.
2). Suppose further we go to the 2nd stage. Should we use data from the 2nd stage to estimate CI for AUC the second time? If yes, it possibly can fail, if not - how to explain the fact that we do not use the data?
Another example: b). First step - estimated power is less than target (80%) for both metrics and adjusted level CI is outside the range. Should we use the largest observed CV to calculate the total sample size? Would the study be overpowered for the second PK metric? Would it affect the TIE?

To conclude: what is the best strategy to follow in this situation in order to avoid inflation of the TIE and the loss of power?

(Some mad idea: is it possible to make some hybrid monster to combine both Cmax and AUC in the same test for adaptive designs? Something like Cmax/AUC but with more powerful reflection of the situations (I dealt with a plenty of studies (BE and not proven BE) with Cmax/AUC as an additional metric, only once it was outside the range)

» Furthermore, a formulation-by-stage interaction term in the model is considered nonsense in the EMA’s Q&A.

What ANOVA model should be used for the second stage? By the way, what about the code on R for the full decision tree? ;-)

"Being in minority, even a minority of one, did not make you mad"

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