## Educate the IEC and regulators [Power / Sample Size]

Hi Yura,

❝ You are considering the power after the study at n = 28 (which were calculated before the study: GMR=0.95, CV=0.25, β=0.80). The question is whether it is possible to carry out a study at n = 50 and will this be forced bioequivalence?

As I wrote above the IEC and the authority should judge this before the study is done. I agree that in many cases the statistical knowledge of IECs is limited. However, once the protocol was approved by both, I don’t see a reason to talk about “forced BE” any more.

BTW, I don’t see a problem if a study is designed for 90% power (80% is not carved in stone). Let’s assume a dropout rate of 15% and we will already end up with 46 subjects:

library(PowerTOST) CV     <- 0.25 # CV-intra theta0 <- 0.95 # T/R-ratio target <- 0.90 # desired (target) power dor    <- 15   # expected dropout rate in percent n      <- sampleN.TOST(CV=CV, theta0=theta0, targetpower=target,                        print=FALSE)[["Sample size"]] ceiling(n/(1-dor/100)/2)*2 # round up to next even

Considering your example and assuming that the GMR and CV turn out exactly as assumed, no dropouts (n=50): The 90% CI will be 87.47–103.18%. Fine with me. Not even a significant difference (100% included). If the drop­out-rate is as expected (n=38) the 90% CI will be 86.36–104.51%. If the assessor is not happy with that, he should have a chat with his colleague who approved the protocol and enlighten him about potential “over-powering” in study planing.
According to all guidelines (CI within the acceptance range) I can’t imagine a justification to reject the study. If the study is not accepted only due to the high sample size in the EEA the applicant might go for a referral (with extremely high chances of success) and in the USA the FDA will be sued right away.

Dif-tor heh smusma 🖖🏼 Довге життя Україна!
Helmut Schütz

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