Bootstrapping BE: Desultory thoughts [Study As­sess­ment]

posted by Helmut Homepage – Vienna, Austria, 2021-07-09 16:50 (1106 d 05:48 ago) – Posting: # 22465
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Hi ElMaestro,

❝ The term "robustness" lost its meaning to me some months ago following an opinion expressed by a regulator in relation to robustness and simulation.

Oh dear! Any details?

❝ […] in dissolution trials we can often do a simple f2 comparison and if it "fails" then bootstrapping of the same data is the final attempt and it may sometimes actually lead to approval.

Regrettably the other way ’round (see there). An ƒ2 ≥50 does not guarantee acceptance any more. The lower bootstrapped CL  has to be  is preferred to be ≥50 as well.

❝ An area where I think bootstrapping is totally ok and very useful is when you want to derive a sample size and you have pilot trial data. If the residual is totally weirdly distributed in the pilot trial, then a sample size calc. in the classical fashion can be wasted time and effort even though the final study always has to be evaluated in the usual parametric fashion involving the assumption of a normal residual.

Agree – in principle.

❝ This is where a bootstrap sample size approach can be very justified and useful. But it, too, has some shortcomings. Such as the assumption of the GMR. You can't easily make provisions for assuming some other than what you have seen in the pilot. Nasty. :-D

Yep. Another issue are ‘outliers’ like in my example. Does it make sense to assume to face them in the pivotal as well? I hope not. Then what? Drop them from the pilot data and bootstrap that?

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