Explained crap remains crap [Two-Stage / GS Designs]

posted by Helmut Homepage – Vienna, Austria, 2020-02-17 13:49 (1561 d 07:23 ago) – Posting: # 21176
Views: 6,263

Dear Detlew!

❝ I'm quite sure: This is because of the simulation error. The differences of the TIE without and with min.n2 are so small.

❝ Any try with a different seed of the random number generator may and will change the comparison.

As usual you are right. :thumb up:
The standard error of a single estimate from 1 mio simulations is \(\small{\sqrt{0.5\alpha/10^6}\approx 0.00016}\). With random seeds results spread but the trend is obvious:



25 replicates; blue dots fixed seeds, light blue dots random seeds. Model fits, 95% and 99% prediction intervals.

Walking in the footsteps of zizou and trying an argument:Since the α 0.0294 of Potvin’s1 Method B is overly conservative, ANVISA’s requirement fortunately controls the Type I Error (see the first plot above) but this might not be the case with other methods where the adjusted α gives a TIE closer to the nominal 0.05.

Consequences for the Consulta Pública N° 760:
  1. The minimum n2 of two subjects given in the EMA’s Q&A document is nonsense for obvious reasons: If a second stage can be initiated (study failed in stage 1 and interim power below target), any software will come up with balanced sequences. What’s the minimum? Guess.
  2. Sponsors will like the increased power (see the second plot above). However, regulators should be interested in protecting the public health and not the profits of the industry.

  1. Potvin D, DiLiberti CE, Hauck WW, Parr AF, Schuirmann DJ, Smith RA. Sequential design approaches for bioequivalence studies with crossover designs. Pharm Stat. 2008; 7(4): 245–62. doi:10.1002/pst.294.
  2. Maurer W, Jones B, Chen Y. Controlling the type 1 error rate in two-stage sequential designs when testing for average bioequivalence. Stat Med. 2018; 37(10): 1587–607. doi:10.1002/sim.7614.

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