Bioequivalence and Bioavailability Forum

Hi MGR,

❝ Here in our company WinNonlin 5.2.1 is installed, So i have to do analysis using the same version.

I don’t know how inspectors would judge the fact that your company is using an obsolete soft­ware (which is no more supported by Pharsight since 2011). The upgrade to Phoenix was free of charge for years. Talk to your management!

❝ Done the analysis, but the problem is that, 94.12% confidence interval values are coming same when subject(sequence) is treated as fixed effect and another with random effect.

As expected…

❝ Could be please explain why this happened?

The CI is calculated from the geometric mean ratio, the residual error, and the degrees of free­dom (in a 2×2 cross-over n–2, where n is the total number of subjects). These values are iden­ti­cal in both models.¹ My results for the first stage of Potvin’s Example 2, Method B in Phoenix / Win­Nonlin 6.3 – agreeing with what the authors reported:

• Fixed Sequence + Treatment + Period    Random Subject(Sequence)

Ratio:     108.76%
94.12% CI:  92.93 – 127.28%
  
  
• Fixed Sequence + Treatment + Period + Subject(Sequence)

Ratio:     108.76%
94.12% CI:  92.93 – 127.28%
  

“Philosophically” speaking treating subjects as a random effect allows prediction of the popu­la­tion’s expected response based on the study’s sample. Treating subjects as a fixed effect makes only a statement about subjects in this particular study. See also this post.

EMEA. The European Medicines Evaluation Agency.
   The drug regulatory agency of the European Union.
   A statistician-free zone.    Stephen Senn (2004)

However, for the EMA you have to stick to the all fixed effects model with Subject(Sequence).² My ex­pe­ri­ences from a last year’s MRP (mixed effects model, Method C passed BE in the first stage): Accepted by the RMS Germany; no comments from the CMS Austria, Denmark, Sweden, and The Nether­lands. Nonetheless, Spain:

“Statistical analysis should be GLM. Please justify.”

Can you guess the outcome of the all fixed effects model presented in the response letter?

---

1. Identical both for balanced (n₁=n₂) and imbalanced (n₁≠n₂) studies. However, dif­fe­rent re­sults for in­com­plete data (missing periods). In SAS-lingo Proc GLM ≠ Proc Mixed. Not an issue here (according to the EMA incomplete subjects have to be excluded in 2×2 cross-over studies).
   
2. For aficionados: ElMaestro does not get tired pointing out that sub­jects are un­am­bi­gu­ously coded to se­quen­ces in cross-over BE studies. Therefore, nesting Subjects into Se­quen­ce is super­flu­ous. Homework: Try what happens with the more simple Sequence + Treatment + Period + Subject.