Bioequivalence and Bioavailability Forum

Hi kimhuang,

❝ My so-called PK parameter analysis refers to the simple descriptive statistics of PK parameters (such as AUC, Cmax, Tmax, etc. for mean, standard deviation, median, minimum value, maximum value, etc.), which does not involve the BE evaluation between the two treatment, whether PK descriptive analysis set and BE evaluation set must be consistent?

According to ICH M13A Section 2.2.3.2 you can use a model with all effects fixed of the per protocol data for assessing BE or a mixed effects model of all data (even if only one period). A mixed effects model (1) is preferred by the FDA, Health Canada, and China’s CDE, where­as a model with all effects fixed (2) of the per protocol data is preferred in all other jurisdictions. Theoretically both methods should be acceptable but I suggest not to lure assessors out of their comfort zone.
Therefore, the short answer to your question: Yes. Always give appropriate location and dispersion statistics reflecting the distributional properties, i.e., for log-transformed PK metrics the geometric mean and CV instead of the arithmetic mean…

1. Descriptive statistics of all data. Easy.
   
2. Descriptive statistics of all data and of the per protocol data (only complete ones).

State in the text that the point estimate of the treatment effect from the BE model will be close but not identical to the ratio of geometric means of treatments. In a mixed effects model it is based on Restricted Maximum Likelihood (REML). This statement avoids questions from assessors equipped with a pocket calculator. 

❝ BTW, in FDA draft guidance «Statistical Approaches to Establishing Bioequivalence Guidance for Industry»…

❝ Will FDA guidance be updated with the ICH M13A statistical perspective? Or will FDA accept the two pre-specified missing data handling methods?

I guess both but will prefer the latter. ICH M13A gives no details in Section 2.2.2.2, only:

Summary statistics to be reported include number of observations, geometric mean, coefficient of variation, median, arithmetic mean, standard deviation, minimum, and maximum.