Power<50% [Power / Sample Size]

posted by BEQool  – 2024-03-02 00:02 (80 d 05:22 ago) – Posting: # 23889
Views: 744

(edited by on 2024-03-02 08:47)

CI.BE() gives us the realized results (i.e., the values observed in a particular study), whereas power.scABEL() the results of 105 simulations. Both the CV and PE are skewed to the right and, therefore, I would expect that simulated power is lower than assessing whether a particular study passes. However, such a large discrepancy is surprising for me.


What about function power.TOST()? I think that it doesnt give us the result based on simulations? Am I right? And this calculated power is also way below 50% (similar to the power calculated above):

power.TOST(theta0=0.95,n=36,design="2x2x3",CV=1.1, theta1=0.6983678, theta2=1.4319102)
0.2355959


I can find more such examples :-) All are as expected close to the limit:
a) CI.BE(pe=0.95,n=36,design="2x2x3",CV=0.5)
    lower     upper
0.8089197 1.1156855


power.TOST(theta0=0.95,n=36,design="2x2x3",CV=0.5)
0.4273464


b) CI.BE(pe=0.95,n=36,design="2x2",CV=0.44)
    lower     upper
0.8033552 1.1234134


power.TOST(theta0=0.95,n=36,design="2x2",CV=0.44)
0.3786147


c) CI.BE(pe=0.94,n=18,design="2x2",CV=0.28)
    lower     upper
0.8011079 1.1029725


power.TOST(theta0=0.94,n=18,design="2x2",CV=0.28)
0.4259032


...

❝ If any of the metrics shows power <50%, the study will fail (see this article).


Is it possible that the opposite is always true and this isnt?
So that if the study fails, the power will always be <50%; but if the power is <50%, it doesnt always mean that the study failed (will fail)?

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