## Optimists and pessimists [Power / Sample Size]

Hi David,

» I think the answer to the first question of this post is "because you were very pessimistic on your assumptions regarding sample size" which is something very common in BABE trials (at least, this is my perception).

Ha-ha! I got too many failed studies on my desk and my clients think that I’m Jesus and can reanimate a corpse… In most cases they were overly optimistic in designing their studies.

» In this case, given your simulations above and the expected probability of approximately 12% of the studies having power greater then 95% …

~15%!

» … having in consideration the initial assumptions, post hoc power means nothing.

Yep.

» But if you had 100 studies instead and 90% of the had >95% power although the sample size was calculated assuming expected power of 80%, some questions and conclusions might be drawn from those results, don't you think?

Agree.

» From my understanding of the initial question, this was the case found. So I think that they should start by reviewing how they define their assumptions for the sample size, namely why they assume GMR=1.10 instead of the "normal" 0.95/1.05.

Well, the current GL is poorly written. Talks only about an “appropriate sample size calculation” [sic]. The 2001 NfG was more clear:

The number of subjects required is determined by

1. the error variance associated with the primary characteristic to be studied as estimated from a pilot experiment, from previous studies or from published data,
2. the significance level desired,
3. the expected deviation from the reference product compatible with bioequivalence (∆) and
4. the required power.
Taking into account that the analytical method used for measuring the content of test- and reference-batches has limited accuracy/precision (2.5% is excellent!) and is never validated for the reference product (you can ask the innovator for a CoA but never ever will get it) 0.95 might be “normal” but IMHO, optimistic even if you measure a content of 100% for both T and R. Given that power is most sensitive to the GMR, I question the usefulness of 0.95.

I’m not a pessimist,
I’m just a well informed optimist.
José Saramago
To call the statistician after the experiment is done
may be no more than asking him to perform a postmortem examination:
he may be able to say what the experiment died of.
R.A. Fisher

OK, I make money acting as a coroner. Wasn’t really successful in the reanimation-attempts.

Dif-tor heh smusma 🖖
Helmut Schütz

The quality of responses received is directly proportional to the quality of the question asked. 🚮
Science Quotes