Reference-scaling: Don’t use FARTSSIE! [Power / Sample Size]

Hi pjs,

❝ For sample size estimation for highly variable drugs, simulations are required to estimate sample size.

Correct.

❝ Just wondering what parameters/variables are applied in the simulations being done like wise in the sample size suggested by Laszlo or by package powertost,

Try the respective functions sampleN.RSABE() for the FDA’s reference-scaling and sampleN.scABEL() for the EMA’s and Health Canada’s average bioequivalence with expanding limits in PowerTOST. Both functions are extensively documented. In the R-console after typing library(PowerTOST) try help(sampleN.RSABE) or help(sampleN.scABEL).
László Tóthfalusi’s code (in MatLab) is not available in the public domain. It is slow (therefore, only 10,000 simulations) and for years the two Lászlós recommend PowerTOST instead.

❝ Also what could be impact of difference in variable assumption for simulation incase of borderline high and very large variability (like 30% and 300%),

With high (say >50%) CwR the GMR-restriction (80.00% ≤ GMR ≤ 125.00%) precedes over the CI-decision. At CVwR 300% practically only the GMR is important (esp. for RSABE).

❝ varying T/R ratios (100%, …

Never, ever assume a T/R-ratio of 100%. It will strike back when the study fails with a too low sample size. We recommend 90% at the best.

❝ … 80.01%)

That’s for CVwR ≤30% (no scaling) almost simulating the Type I Error (assuming true Null). Would be at 80%.
Try

power.scABEL(CV=0.3, theta0=0.8, design="2x2x4", n=34, nsims=1e6) power.RSABE(CV=0.3, theta0=0.8, design="2x2x4", n=32, nsims=1e6)

Surprised?

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