Bioequivalence and Bioavailability Forum

Dear group members!

❝ I remember that I’ve tried to include covariates in BE a couple of years ago and failed as well.

❝ I put it on my ‘to-do-list’, and left it there until today…

❝

❝ Sorry, no better news, consider contacting Pharsight’s support—and post the answer to the forum (I’m curious as well).

After more than a year I finally satisfied my curiosity and requested a solution from Pharsight.
Starting with WinNonlin’s data-set ‘Data22.PWO’ in the ‘Examples’-folder, I added a variable ‘Gender’ which I assigned ‘male’ to the first 10 subjects and ‘female’ to the next 10 subjects.

Pharsight’s support suggested a model with fixed effects for ‘Gender’ and a ‘Treatment by Gender Interaction’.

• Normal BE-assessment, reference='Capsule' (residual DF) gets for
  Fixed effects: Sequence+Formulation+Period
  Random1: Subject(Sequence)
  Ratio 104.6588, 90% CI (92.2133, 118.7840)
  
• Covariate included
  Fixed effects: Sequence+Formulation+Period+Gender+Formulation*Gender
  Random1: Subject(Sequence)
  Ratio 104.6588, 90% CI (92.7438, 118.1045)
  Partial tests for Gender p 0.3303, Formulation × Gender p 0.1082.

No large differences are to be expected even for drugs with large gender differences in BA, because BE is a within subject comparison.
A similar model may be used if the study was performed in two or more groups (e.g., for logistic reasons). Just use 'Group' instead of 'Gender' in the model. If the 'Treatment × Group Interaction' term is statistically not significant (p<0.05), the simple model (i.e., naive pooling of data) can be applied.