Mikalai ☆ Belarus, 20180606 06:28 (edited by Mikalai on 20180606 06:44) Posting: # 18854 Views: 2,742 

Dear all, We plan to conduct a sequential (two stage) BE study, and I am concerned with "forced bioequivalence". Specifically, if we obtain nonequivalent results in the first stage with very low power and should recruit more volunteers, how can we protect ourselves from getting into "forced bioequivalence"? In other words, how can we differentiate between underpowered trials and nonequivalent results in the sequential BE? And how can we put this (protection against "forced bioequivalence") in the protocol not to raise many questions from regulators? Any suggestions and advice will be appreciated. Sincerely, Mikalai 
ElMaestro ★★★ Belgium?, 20180606 08:53 @ Mikalai Posting: # 18855 Views: 2,453 

Hi Mikalai, » We plan to conduct a sequential (two stage) BE study, and I am concerned with "forced bioequivalence". Specifically, if we obtain nonequivalent results in the first stage with very low power and should recruit more volunteers, how can we protect ourselves from getting into "forced bioequivalence"? In other words, how can we differentiate between underpowered trials and nonequivalent results in the sequential BE? And how can we put this (protection against "forced bioequivalence") in the protocol not to raise many questions from regulators? If you use one of the Potvin variants (and you will do that, no discussion) then forced bioequivalence is not your biggest issue. Bear in mind that your "very low power" is still based on a fixed GMR of e.g. 0.95, not on the observed GMR. I don't think it is a good idea to fiddle with the Potvinlike decision trees. I mean, if you do a little, minor, innocent modification without running a series of tests for power, sample size and type I error, then all manners of hell can break loose on you. I have seen it several times now. Forced BE is not a term that is widely adopted from the regulatory side. If the (true) GMR is within 80.00125.00 then in principle you have a product for which you can one way or another show BE and which can be approvable. Obviously you will never know the true GMR, only you can estimate it through observations which have a variance, hence the need for a CI. Your twostage approach is great if you are certain about the GMR (close to 100%) and uncertain about the CV. If you are not convinced that you have a good GMR, then lay your hands off the twostage approach. Run like hell. It will 'on average' not work well for you. — Le tits now. Best regards, ElMaestro 
Yura ★ Belarus, 20180607 08:24 @ ElMaestro Posting: # 18859 Views: 2,373 

Hi Mikalai, 'forced bioequivalence'  it's not from "that opera". If you took for 2х2х2 n = 120 for CL AUCt (90111.11  with narrow therapeutic index), CV = 16.5% and recommended GMR = 97.5%, while in calculation (in R) for 2х2х4 you get n = 30. The sample size is twice as high as possible, which is not ethical  to expose an unknown effect of the test drug to more people than necessary. If I understand correctly, that's it 'forced bioequivalence' regards 
ElMaestro ★★★ Belgium?, 20180607 11:53 @ Yura Posting: # 18863 Views: 2,339 

Hi Yura, you do your study as best you can, making some assumoptions good or bad about GMR and CV. At the end of the day you may show BE or not, and if you do, then it may be with a large or small margin. I guess forced BE just means the margin was large whatever that means quantitatively. There is no real issue here. The discussions I have seen about BE consider forced BE as a hindsight phenomenon, like posthoc power. If you start fiddling with "forced BE" being convincingly planned before a trial then I would of course oppose it. Remember: In principle, either the product is BE or it isn't. There just happens to be some uncertainty on the degree by which we can demonstrate it. — Le tits now. Best regards, ElMaestro 
Yura ★ Belarus, 20180607 12:59 @ ElMaestro Posting: # 18864 Views: 2,297 

Hi ElMaestro, Why then estimate the size of the sample? "Take more, throw farther" regards 
Mikalai ☆ Belarus, 20180607 13:47 @ Yura Posting: # 18865 Views: 2,294 

Good afternoon everyone I just would like to clarify a bit the situation. We have a drug and do not know it CV. We take an arbitrary sample and then calculate the CV and real GMR in the first stage. In the second stage, we, if I understand correctly, should calculate posthoc power and recalculate the sample size with the data (GMR and CV) obtained in the first stage, if bioequivalence has not been achieved in the first stage. But what to do, if we have got bad GMR(0,83), whatever CV and low power (around 30) in the first stage. In this case, as I understand, we should recruit much more subjects according to our protocol. The questions, how can we avoid slipping into "forced bioequivalence"? Or should we go straight away and recruit this large number of subjects? What should be put into the protocol? Best regards, Mikalai 
Helmut ★★★ Vienna, Austria, 20180607 15:33 @ Mikalai Posting: # 18866 Views: 2,386 

Hi Mikalai, » We have a drug and do not know it CV. We take an arbitrary sample … Well, I always try to make an educated guess of the CV. If you aim too low in the first stage, the sample size penalty in the second stage will be larger. Example: “Guesstimate” CV 25%, Potvin B, n_{1} 12 or 24.
With 12 subjects in the first stage on the average you will have a total sample size of 32.4 (median 32) and with 24 only 29 (median 24). In the latter case you have already a chance of 63% to show BE in the first stage and in the former only 18%. » … and then calculate the CV and real GMR in the first stage. Yep. But in ‘Type 1’ TSDs you generally ignore the observed GMR and work with a fixed (assumed) T/Rratio. » In the second stage, we, if I understand correctly, should calculate posthoc power and recalculate the sample size with the data (GMR and CV) obtained in the first stage, if bioequivalence has not been achieved in the first stage. Nope. You calculate interim power after the first stage. If you want to use the GMR of the first stage as well (go fully adaptive) you might shoot yourself in the foot. Practically you need two futility criteria:
» But what to do, if we have got bad GMR(0,83), whatever CV and low power (around 30) in the first stage. You are free to include futility criteria for early stopping in the method. You don’t have to worry about the adjusted α because any futility criterion decreases the patient’s risk. » In this case, as I understand, we should recruit much more subjects according to our protocol. In general you should not give a total sample size in the protocol – unless it is part of the framework (simulations recommended: have an eye on power). If you are courageous try the InverseNormal Combination Method / Maximum Combination Test (Maurer et al. 2018). Pro: Proven to preserve the Type I Error (makes regulatory statisticians happy). Con: Might be the first time they ever have seen sumfink like this. Expect questions. Example: Like above but two futility criteria: GMR within [0.8, 1.25] and maximum total sample size 120. GMR observed in the first stage used (fully adaptive).
» The questions, how can we avoid slipping into "forced bioequivalence"? Or should we go straight away and recruit this large number of subjects? What should be put into the protocol? As ElMaestro wrote above, I don’t see how you could run into “forced BE”.
— Cheers, Helmut Schütz The quality of responses received is directly proportional to the quality of the question asked. 🚮 Science Quotes 
Mikalai ☆ Belarus, 20180608 10:24 @ Helmut Posting: # 18871 Views: 2,270 

Dear Helmut, Thank you very much for your explanation. Could you, please, clarify a bit further? » Nope. You calculate interim power after the first stage. If you want to use the GMR of the first stage as well (go fully adaptive) you might shoot yourself in the foot. Practically you need two futility criteria:
Are there any rules or recommendations for setting up the prespecified limit (U) as a futility criterion? Regards, Mikalai 
Helmut ★★★ Vienna, Austria, 20180608 12:00 @ Mikalai Posting: # 18874 Views: 2,308 

Hi Mikalai, » Are there any rules or recommendations for setting up the prespecified limit (U) as a futility criterion? Not really. You have to find a balance between the maximum study costs you are accepting to spend and the potential loss in power. Xu et al.* recommend a futility of 42 on n_{total} for CV ≤30% and 180 for CV >30%. Generally a small stage 1 sample size is not a good idea.
Remember that if you deviate from one of the published methods (except by adding a futility which leads to early stopping) you have to assess the Type I Error. Fine with the setting above:
The maximum inflation of the TIE is often observed at combinations of small n_{1} and low CV. The minimum n_{1} for Xu’s method is 18. With CV 10% we get a TIE of 0.035744.
— Cheers, Helmut Schütz The quality of responses received is directly proportional to the quality of the question asked. 🚮 Science Quotes 