Bioequivalence and Bioavailability Forum

Hi Mittyri,

❝ ❝ The statistical model should take into account the multi-group nature of the BE study, e.g., by using a model including terms for group, sequence, sequence × group, subject within sequence × group, period within group and formulation. The group × treatment interaction term should not be included in the model. However, applicants should evaluate potential for heterogeneity of treatment effect across groups and discuss its potential impact on the study data, e.g., by investigation of group × treatment interaction in a supportive analysis and calculation of descriptive statistics by group.

❝

❝ I hope that we can see other improvements in the future.

Unlikely. ICH guidelines regularly are not updated for 20+ years (e.g., E3 of 1995, E8 of 1997, E9 of 1998).

❝ – do you know the reason to include period within group and formulation? As far as I remember, Model I and Model II included Period(Group) factor only.

Models as stated by the FDA and used by the usual suspects (see this post):

1. Group, Sequence, Treatment, Subject(Group × Sequence), Period(Group), Group × Sequence, Group × Treatment
   
2. Group, Sequence, Treatment, Subject(Group × Sequence), Period(Group), Group × Sequence

Model II is the one of the guideline, only the order of effects is different. Doesn’t matter. Model I should not be used.

I don’t understand what is meant by »investigation of group × treatment interaction in a supportive analysis«. Assess for BE by Model II and then the \(\small{G\times T}\) interaction by Model I? We will find a significant interaction in ≈ 5% of studies (i.e., at the level \(\small{\alpha}\) of the test). Lengthy and fruitless discussions expected.

❝ – What kind of descriptive statistics is expected? Everything above BE section stratified by group?

I think so.

❝ What are the consequences of this evaluation of heterogeneity? Is it possible that assessor will be unhappy with the statistics of some group comparing to others? Since no tests are suggested (eyeball desc stat by group comparison?), …

Even if an assessor would calculate the confidence interval of groups separately, likely they would overlap due to the limited sample sizes. So what?

A recent example (4 period full replicate design):


The study was equivalent with Model II, though it was a close shave (PE: 116.50%, 90% CI: 109.32–124.16%)…

❝ … any evidence provided post hoc are weak arguments

Even not acceptable: Section 2.2.3.1 (page 11)

• The statistical analysis should take into account sources of variation that can be reasonably assumed to have an effect on the response variable. Post hoc and data-driven adjustments are not acceptable for the primary statistical analysis.

As already in the draft (page 16).