Bioequivalence and Bioavailability Forum

Hi Steven,

❝ I just fail to intuitively understand, how the intraindividual variance for each product can be calculated without a replicated administration.

In a mixed-effects model (which we apply here) you could get this information. The method is similar to recovering information from incomplete data. Compare

• A dataset with one period missing for one or more subjects. Run a mixed-effects model
  (subject random).
  
• Exclude the subject(s) with missing periods and run the all fixed-effects model
  (subject fixed).

The CI (and CV_intra) likely will be pretty similar.

❝ ❝ What do you mean by “look different”?

❝

❝ […] the variances given for Test and Reference as “1_2/2_1” looked “different”, e.g. with 0.0113 and 0.0061 in one of my data sets.

Hhm, not sure which coding you used. It should be: Random: Subject(Sequence) and Repeated: Period, Subject, Treatment. Have a look at the Parameter Key-table to find out treatments’ coding. With my coding Var(Period*Treatment*Subject)_21 is s²_wR and Var(Period*Treatment*Subject)_22 is s²_wT.

❝ And still we have to submit the appropriate tests of all effects in the model. Hm, makes me wonder, if authorities are actually looking into the Core outputs regularly…

ElMaestro would say that chances are 0.0000001% or lower. At least in the EU deficiency letters of the type “There is a significant sequence effect in ANOVA. Please justify.” almost stopped.

❝ And when we use the simple Core output, testing by PM will then be included also, as “The results […] are given in the Average Bioequivalence output worksheet and at the end of the Core output {Users Guide 6.4}.”

Yep. I always use the core output myself (M$-Word export is awful). In v6.3 and earlier I deleted irrelevant or obsolete stuff (Westlake’s CI, Anderson-Hauck, “Power”). Had an SOP for it. Now I have an R-script for that.

❝ From what I learned from other people, I can understand the condemnation of using a fixed effect for subjects.

If one looks only at the numbers, results are the same. Since I don’t want to make a statement about subjects in this particular study only but to extrapolate to the population I prefer a random effect.

❝ From some reports I saw I personally can appreciate, that in a 2x2 setting subjects who cannot contribute to the T/R comparison (missing data in one/two periods) will be omitted “automatically” instead of getting the missing data imputed. That’s a nice side effect making the evaluation keeping in line with the EU-GL.

Yes. In v6.4 you can set it in the Preferences.
LinMixBioequivalence > ☑ Default for 2×2 crossover set to all fixed effects