Bioequivalence and Bioavailability Forum

Dear D Labes!

❝ • From where do we get the intra-individual variabilities within Test or

❝   Reference? One of the ANOVA assumptions is equal variabilities within

❝   groups I think.

In a 2×2 design that's a main assumption. In replicate designs I'm not sure.

❝ • Can we have an evaluation that is not only EMA accepted?

I don't think so.

❝ […] mixed model […] obsolete in the light of the new EMA guidance!

❝ So how to react? How to proceed with replicate studies practically, technically ... to be EMA conform?

To be honest: not the slightest idea.
I'm still trying to track down where the ANOVA comes from. Tóthfalusi et al. (2009) wrote in Section 6.1 (p 737):

  The statistical models underlying the calculations make several assumptions. Relaxation and modifications of these assumptions can lead to different analyses and results. The issue of the multiplicity of models is particularly troublesome when parameters are estimated from replicate designs (with three or more periods), because each model corresponds to a different statistical and computational procedure and provides different final estimates and conclusions. Bioequivalence studies are not powered sufficiently to check the validity of most of the model assumptions, and thus the different assumptions can lead to multiple solutions. Each of the solutions can be correct statistically, but it is problematic if the different solutions suggest contradictory regulatory decisions. In the case of simple two-by-two bioequivalence studies, the data are analysed in a straightforward manner, and the computational procedure is simple and unique. This is not true for replicate crossover trials. Statistical models of a replicate crossover trial can be written in different ways. In borderline cases, this requires that regulatory agencies clearly specify their preferred method of analysis. As an alternative, Hsuan and Reeve have described a unique ANOVA type of estimation method.

Note the last two sentences! But as a statistical amateur I don't get the point looking at the referred paper:

Hsuan FC, Reeve R. Assessing individual bioequivalence with high-order cross-over designs: a unified procedure. Stat Med. 2003;22:2847–60.

From the summary:

The U.S. FDA's newly issued guidance on bioequivalence recommends the use of individual bioequivalence (IBE) for highly variable drugs and possibly for modified release dosage forms. The recommended approach to the analysis is to follow the methodology of Hyslop, Hsuan and Holder (HHH), based on a linear mixed model. A limitation of the HHH method is that it works only for uniform designs, such as RTRT/TRTR. In this paper, we present an alternative approach based on a multivariate model. The multivariate model is shown to be a strict superset of the linear mixed model and can successfully model data where the mixed model fails. Our multivariate approach coincides with the HHH method where the HHH method applies, but generalizes to any high-order cross-over design, such as the Balaam design, RTR/TRT, and TRSS/RSTT/STRR.

My emphasis. And on page 2858:

  The method we proposed here has been implemented in a Pharsight™ product, WinNonlin^® (version 3.2 or later).

Too bad that WinNonlin's output states on top of each page

WINNONLIN LINEAR MIXED EFFECTS MODELING / BIOEQUIVALENCE

Maybe I write a one-line SOP: "In the core output replace 'LINEAR MIXED EFFECTS MODELING' by 'MULTIVARIATE ANOVA'; save file."