Bioequivalence and Bioavailability Forum

Dear Helmut,

❝ I’m asking myself how to interpret this part of the BE-GL in the section Subject accountability:

Ideally, all treated subjects should be included in the statistical analysis. However, subjects in a crossover trial who do not provide evaluable data for both of the test and reference products […] should not be included.

Funny that the first sentence describes an “ideal” situation which can be handled only with a mixed effects model – which at least for conventional crossovers is taboo.

When subject is vomiting in the first period early after administration and then has e.g. flu. Neither mixed can't help.

❝ I would say that the second sentence was written having nonreplicated crossovers in mind.

Who knows...
EMA mentioned it in Q&A (page 15) also:

The question of whether to use fixed or random effects is not important for the standard two period, two sequence (2×2) crossover trial. In section 4.1.8 of the guideline it is stated that “subjects in a crossover trial who do not provide evaluable data for both of the test and reference products should not be included.” Provided this is followed the confidence intervals for the formulation effect will be the same regardless of whether fixed or random effects are used.

So maybe not. With interest in intra-subject variability I think that test and reference formulations will differ more than only two references in the most of the subjects. Hence the exclusion of subject without T and R data concurrently (i.e. the exclusion of subject with only RR data) will have the impact that the intra-subject CV of all (pooled) data would be higher (more probably). It's because we exclude subject with two RR which probably don't differ so much as T versus R. At least I guess and I see the point in that. To be on the safe side - exclude all these RR subjects from T/R evaluation (as these subjects could affect the intra-subject CV to be lower "incorrectly" based on RR differences of such individual subjects, i.e. affect the 90% CI to be narrower).
Of course it can happen as in your data example that CV_W will be lower (i.e. 90% CI narrower) after exclusion of only RR subject(s) but I would expect the opposite really more often.

❝ We discussed in the forum whether subjects with only RR-data (...) should be included for the estimation of CV_wR and the consensus was: yes.

Yes from me too if the subject is not outlier - which is other discussed topic with no guideline with definition of the outlier.

(I am thinking in method A as the simplest method to think about and as EMA.)
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(?) and I didn't give much time to method C myself to get all points in that method mainly because of method A is preffered by EMA).

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:
proc glm data=scavbe;
class seq;
model ilat=seq/clparm alpha=0.1;
estimate 'average' intercept 1 seq 0.3333333333 0.3333333333 0.3333333333;
ods output overallanova=iglm1;
ods output Estimates=iglm2;
ods output NObs=iglm3;
title1 'scaled average BE';
run;
Note: Red color doesn't mean wrong here.

With the fact that FDA do not round off the 90% CI limits it is quite shocking that precision on 10 decimal places is enough here!

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.)

A little fun.
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. x)

Best regards,
zizou