Bioequivalence and Bioavailability Forum

Dear Helmut,

The following message was supposed to be posted here yesterday. However, I just could not submit it after I finished it. The forum was hanging there without any response. I wished that I did not do anything stupid at that moment.
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Thank you for your explanations. Very nice comments.

❝ entire alpha-risk in the interim looks. In other words - you must not evaluate the study for BE (i.e., calculate the 90% CI and check

My guess is this procedure will not be done unless the power is ≥ 80% (Method C, as the left branch of the flowchart). Do you mean that we should not do this when the power is < 80%? If doing so, I guess the answer is still not BE, isn't it? The question can be if the regulatory agents accept BE when the power < 80%, and evaluate BE at stage 1 with alpha = 0.0294 (Method C, as the left sub branch of the right branch) when we analyze the data as the fixed-sample trial. BTW, I don't know what possibility of getting BE will be with alpha = 0.0294, when it has been not BE with alpha = 0.05. It is more stringent (0.0294 vs. 0.05), isn't it?

❝ for inclusion). If you do that, your entire alpha-risk has gone - nothing left for further looks. This was actually the problem with the Canadian and Japanese method.

What kind of the problem with the Canadian and Japanese method can be?

❝ Yes, according to Potvin et al. It's important to notice that the value calculated here is not some kind of a posteriori power...

Yes, the calculation equation for the power is different and complicated.

❝ 'round: If you just miss the 80% (let's say you had one drop-out; power 79.1%). Power for alpha 0.0294 is 69.3% and that's still a pretty good chance that we will show BE at Stage 1 and stop. Only if we fail here, we will advance to Stage 2.

Smart choices.

❝ (literature data only or small pilot study), would you really want to 'save' 10% of the budget and end up with an upper CI of 125.94%. My suggestion is to power the first stage of the study as if it is a fixed- sample design. If expectations come true - business as usual: conventional statistical model, 90% CI, everybody's happy. If not, you get a second chance!

Very persuasive. only extra 10% of the total costs of the fixed-sample design? or it depends how many subjects should be recruited at stage 2?

❝ In principle, yes. It is important that you don't fall into the trap of calculating the sample size based on the point estimate of Stage 1...

O.k.. May I ask why WNL does not implement the data analysis for the two-staged design if FDA/EU have been able to accept the two-staged designs? or it has been implemented with WNL (v6.x)?

❝ this thread). See also the last sentence of the Two-stage design Section of EMA's GL: When analysing the combined data from the two stages, a term for stage should be included in the ANOVA model.

This should be no problem with lm() function with R (as PROC GLM with SAS) using stage, sequence, period(stage), trt, and subj(sequence*stage) effects in the model (p. 253 in Potvin D, et al., 2008).