Bioequivalence and Bioavailability Forum

Dear Ben!

❝ nice slides

THX!

❝ Now, my question is whether this approach using the 95% upper limit can also be rephrased (equivalently) in an "easier" way, that is, in terms of 90% CI must fall within exp(± k*σ_WR) (using just a different k). On these slides from the FDA it seems that it is the same as scaling the acceptance range using k=0.893. Is it really equivalent?

First see this thread and the references within. Also a recent paper:

Karalis V, Symillides M, P Macheras P. Bioequivalence of Highly Variable Drugs: A Comparison of the Newly Proposed Regulatory Approaches by FDA and EMA. Pharm Res. 2012;29(4): 1066–77. doi:10.1007/s11095-011-0651-y

FDA’s switching condition θ_s is ~0.893 – based on a standardized variation σ₀ of 0.25: ln(1.25)/0.25 ~ 0.89257… In principle you are right that the scaled limits could be derived according to U,L = e^{±0.893·σ_WR}, but applied only if CV_WR>30% – which leads to the nice discontinuity at CV 30%:
CV%        L      U
30        0.8000 1.2500
30.00001  0.7694 1.2997
Now for the tricky part. Which model would you use to obtain CV_WR (or σ_WR)? FDA wants to see SAS Proc GLM for the partial replicate (scaled) and SAS Proc Mixed for the full replicate and both designs in (unscaled) ABE. From EMA’s (in)famous ‘data set I’ we obtain with FDA’s RSABE code CV_WR 46.96% (s_WR 0.4464) and with the ABE code CV_WR 47.33% (s_WR 0.4496). I guess we should use the former. From the intra-subject contrasts of T vs. R ‘ilat’ we can not only derive the PE, but also its CI:
             CVWR    sWR    L      U      PE       90% CI
RSABE code  46.96% 0.4464 0.6712 1.4899 1.1546 1.0639 1.2531
ABE code    47.33% 0.4496 0.6693 1.4940 1.1566 1.0710 1.2489

Counter-question: Why would you attempt that?

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PS: Du hast Mehl in deiner Schachtel.