## Power<50% [Power / Sample Size]

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)?