Bioequivalence and Bioavailability Forum

Dear Helmut!

Again !
That's worth a paper.

❝ I’m getting the impression that if PQRI would have had a closer look right from the start (instead of coming up with a ‘one size fits all’ α and playing with a “negligible inflation”), maybe we could have avoided all those effectless discussions we had the last years.

I think their approach was driven by the ability to perform only a limited number of study simulations per day, especially if the number of subjects rises.
Thus they have taken Pocock's 'universal' constant and simply looked what happened. Instead of taking the approach as what it is: These nominal alpha's are lacking any theoretical justification and are used on a purely empirical basis for the BE decision in a crossover with interim sample size adaption. And thus should be adapted to this problem.
As they have done later in method D and Montague’s paper.
And as Anders had done in his epoch-making paper
A. Fuglsang
"Controlling type I errors for two-stage bioequivalence study designs"
Clinical Research and Regulatory Affairs, 2011; 28(4): 100–105

❝ BTW, in Montague’s paper I read “The simulations were performed using R […].” Nice to know.

R rulez !

❝ Then “A different randomly selected seed was used for each scenario.” Why? Shall we switch to setseed=FALSE in Power2Stage?

As I read this sentence they used a different seed, randomly selected, but fixed for each scenario (CV, n1) to be protected against artefacts resulting from the starting point of the pseudo-random number generator.
IMHO using a different but fixed seed or a single fixed seed doesn't make a big difference if we simulate with 1E6 sims.
Setting setseed=FALSE in Power2Stage would have the drawback of differing results even for one scenario (CV, n1) if run again.

BTW: What is Anders algo? His C programs?