Bioequivalence and Bioavailability Forum

Hi zizou,

partly answering; have to chew on some other points…

❝ The comparison of methods is over my capability (especially method C which is mixed with specific settings which is not available in many statistical softwares(?)

SAS (and JMP = poor man’s SAS), Phoenix WinNonlin, STaTa, …

❝ Btw. almost everytime I open the FDA draft of progesterone guidance my eyes notice the line (I highlighted it to red) with a little wow:

❝ estimate 'average' intercept 1 seq 0.3333333333 0.3333333333 0.3333333333;

Yes, it hurts.

❝ With the fact that FDA do not round off the 90% CI limits …

Oh, rounding is the FDA’s requirement for ages. Actually the EMA followed this bad practice.

❝ I think more beautiful and more accuracy would be:

❝ estimate 'average' intercept 3 seq 1 1 1 / divisor=3;

❝ (I don't have SAS power so if I am wrong please don't shame me.)

I don’t speak SAS either but I’m sure you looked it up in the online manual.

❝ I know that in mixed methods there are parameters for convergence criteria (e.g. 0.0000000001) or maximum count of iterations which maybe have also little influence on the precision (?) when 90% CI limit is on the board of 80 or 125 %. (Not so easy as EMA method A.)

❝ What if with default settings BE fails and with more precision we get into 80-125%. When BE is recalculated by regulatory (e.g. FDA) with default settings and the result is fail, then BE is challenge.

Correct & good point! Never seen a failure in practice* but it is interesting what the Canadians have to say:

By definition [!] the cross-over design is a mixed effects model [!] with fixed and random effects. The basic two period cross-over can be analysed according to a simple fixed effects model and least squares means estimation. Identical results will be obtained from a mixed effects analysis such as Proc Mixed in SAS^®. If the mixed model approach is used, parameter constraints should be defined in the protocol. Higher order models must be [!] analysed with the mixed model approach in order to estimate random effects properly.

(my emphases)

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• Except sometimes with the f**g partial replicate design. But this is due to the over-specified model (and not related to software). Tweaking the convergence criteria and/or increasing the number of iterations never helped.