rajendra
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India,
2008-04-29 08:56
(6222 d 18:34 ago)

Posting: # 1798
Views: 7,108
 

 power of study [Power / Sample Size]

how to calculate the power of study in bioequivalence?


Edit: Category changed; see also the Policy. [Helmut]
Helmut
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Vienna, Austria,
2008-04-29 13:57
(6222 d 13:32 ago)

@ rajendra
Posting: # 1801
Views: 6,769
 

 No a posteriori power calculations!

Dear Rajendra!

❝ how to calculate the power of study in bioequivalence?

  1. See the Policy.
  2. Power is set to an arbitrary value in study planning, where power=1-beta.
    Beta is the producer's risk (of not being able to demonstrate bioequivalence for a 'true' bioequivalent formulation). Generally values of beta of 10%-20% are used (below 10% 'forced BE' may become an issue, whereas above 20% may rise ethical questions).
Once the study is performed, any 'power calculations' (from the study's results) are futile - either the study was able demonstrating BE or not. A posteriori power does not exist:

Hoenig JM, Heisey DM. The Abuse of Power. The Pervasive Fallacy of Power Calculations for Data Analysis. Am Stat. 2001;55(1):19–24. doi:10.1198/000313001300339897. [image] free resource.


Another nice quote, which may illustrate the situation more clearly:

There is simple intuition behind results like these: If my car made it to the top of the hill, then it is powerful enough to climb that hill; if it didn't, then it obviously isn't powerful enough. Retrospective power is an obvious answer to a rather uninteresting question. A more meaningful question is to ask whether the car is powerful enough to climb a particular hill never climbed before; or whether a different car can climb that new hill. Such questions are prospective, not retrospective.
The fact that retrospective power adds no new information is harmless in its own right. However, in typical practice, it is used to exaggerate the validity of a significant result ("not only is it significant, but the test is really powerful!"), or to make excuses for a nonsignificant one ("well, P is .38, but that's only because the test isn't very powerful"). The latter case is like blaming the messenger.

RV Lenth
Two Sample-Size Practices that I don't recommend
online resource

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