Bioequivalence and Bioavailability Forum

Dear AB,

❝ "NOTE: 20 observations are not included because of missing values.

❝ WARNING: Did not converge."

❝

❝ These missing 20 observations are the ones (AUCI) which were not caculated in the respective treatment groups as the r² values are less than 80%.

❝

❝ However, when we re run the data including these 20 missing AUCI (recalculated without considering the r² criteria), there was no error.

Point 1:
The FDA ABE code for replicate crossover studies using SAS Proc MIXED fits a model which is overspecified for data coming from a partial replicate design. The intra-subject variability of Test formulation is confounded with the subject-by-formulation interaction within that design and not identifiable alone.

This may lead to convergence problems in the REML method (see this thread) or may lead to unreliable values of the intra-subject variances, especially for the Test formulation (see this thread).
Your missing values seems to exacerbate the problem. But are not the source IMHO .

Ways out? Don't know exactly.
The logical way within the code - method - given in the progesterone guidance would be not to use the Proc Mixed code but the estimate plus its 90% confidence interval obtained from the evaluation of the intra-subject contrasts of T vs. R (step Intermediate analysis - ilat) as measure of the ABE.
I'm always wondering why the Proc MIXED code was recommended if the evaluation via intra-subject contrasts did indicate that scaled ABE was not applicable.
On the other hand, if applicable, the ABE criterion evaluated via intra-subject contrasts is part of the scaled ABE criterion. That's illogical to me.

Another possibility is to reduce the model in the FDA Proc MIXED code. Neglecting the subject-by-formulation interaction (setting it to zero) would let to a somewhat better behaving model. You could achieve this by setting the covariance structure within
  RANDOM TRT/TYPE=FA0(2) SUB=SUBJ G;
to CS instead of FA0(2) or CSH.

Point 2:
Not to calculate the AUC(0-inf) values if the fit of the terminal part of the concentration-time curves had an r² value less than 80% is at least statistically not very sound, not to say nonsense IMHO .
Regardless of which study design used.
Use and you will find some (partly lengthy) discussions here in the Forum about that subject. See here and here for instance.