## GMR, theta 0 and that all [Two-Stage / GS Designs]

Hi Silva,

I am not d_labes or Helmut but I have an opinion, too, and you are asking some bloody good questions there

» The algorithm will then calculate the number of simulated studies that wrongly rejected the Null Hypothesis and divide this number by the total number of simulated studies. The ratio represents TIE.

Actually I'd prefer to say it is the maximum type 1 error. The type 1 error is the chance of concluding BE for a product that isn't BE, which is when the true ratio is outside the acceptance range. So when we work with 80.00%-125.00% a product is truly not BE when the true ratio is 72%, 77%, 79%. But these three levels will be associated with different levels of power. Regardless of how a product is inequivalent we aim for methods that give a maximum type 1 error of 5%. By way of the nature of the game, the type 1 error becomes smaller as we part further from one of the limits.

» Considering a study design under Potvin’s method B framework (type I TSD), i.e. and expected GMR of 0.95, an n1 between 12 and 60 subjects, a CV between 10 and 100% and a target power of 0.8, no simulations are required if, on the end of the study, GMR was 0.95 and CV between 10 and 100%. Am I thinking appropriately?

For any approved method I know of it does not have anything to do with what the GMR or CV was at the end of the study as long as you showed BE.

» But if expected GMR is for example 0.91, n1 between 12 and 60 subjects, CV between 10 and 100% and target power is 0.8, there is a violation of method B assumptions, right?

No. See above.

» And therefore simulations are needed based on true data (...)

No, this isn't exactly how it works. The "true data" you refer to are your observations - they give an estimate but they do not give you the true ratio.

This week's list of things I absolutely detest: Corona virus, the which function in R, WIA-WIA interfaces for scanning under Windows 10, the Bee Gees, the smell of my fridge.

Best regards,
ElMaestro