Bioequivalence and Bioavailability Forum

Hi Martin,

the term “fiducial probability” always scares me shitless. Let’s see:
       var.equal=F1  var.equal=T2    PowerTOST1
0.975    0.20600       0.20765       0.2104624
1.000    0.22331       0.22842       0.2288324
1.025    0.23282       0.23900       0.2382360 ← larger than at 1 (all methods)
1.950    0.23159       0.23411       0.2365934
Not sure whether it is correct to simply plug in your sd 0.25 as CV into PowerTOST (though the mean is 1): power.RatioF(alpha=0.05, theta1=0.8, theta2=1.25, theta0=1, CV=0.25, n=20, design="parallel"). However, the same tendency.
Reminds me on the good ol’ days where people started to analyse log-transformed data in BE, but kept the acceptance range at ±20%. Maximum power at 0.9798.
I’m not familiar with this kind of stuff, but doesn’t the analysis work with untransformed data, i.e., Δ ±0.2 → 0.8–1.20?

---

References:
1. Homogenous variances
   Fieller EC. Some problems in interval estimation. J Royal Stat Soc B. 1954:16(2);175–85. online
   
2. Heterogenous variances
   Tamhane AC, Logan BR. Finding the maximum safe dose level for heteroscedastic data. J Biopharm Stat. 2004:14(4);843–56. doi 10.1081/BIP-200035413
   Hasler M, Vonk R, Hothorn LA. Assessing non-inferiority of a new treatment in a three arm trial in the presence of heteroscedasticity. Stat Med. 2008:27(4);490–503. doi 10.1002/sim.3052