Treatment effect justification [General Statistics]
Hi Siva Krishna,
something like this:
"The test producing a significant treatment effect is that log(y)T=log(y)R where y is the dependent variable (AUC or Cmax). This is not the hypothesis evaluated for the conclusion of bioequivalence. It is entirely expected that Test and Reference could be associated with different levels of (logarithmic) Cmax or AUC as they are truly two different formulations. What the evaluation for bioequivalence shows is that the difference in rate and extent and absorption does not differ by a clinically relevant margin, where clinical relevance is determined by the regulatory convention. Therefore the significant formulation effect does not translate, in this case, into inability to conclude bioequivalence."
Good luck.
something like this:
"The test producing a significant treatment effect is that log(y)T=log(y)R where y is the dependent variable (AUC or Cmax). This is not the hypothesis evaluated for the conclusion of bioequivalence. It is entirely expected that Test and Reference could be associated with different levels of (logarithmic) Cmax or AUC as they are truly two different formulations. What the evaluation for bioequivalence shows is that the difference in rate and extent and absorption does not differ by a clinically relevant margin, where clinical relevance is determined by the regulatory convention. Therefore the significant formulation effect does not translate, in this case, into inability to conclude bioequivalence."
Good luck.
—
Pass or fail!
ElMaestro
Pass or fail!
ElMaestro
Complete thread:
- Treatment effect justification Sivakrishna 2020-10-07 08:15 [General Statistics]
- Treatment effect justificationElMaestro 2020-10-07 10:19
- statistically significant ≠ clinically relevant Helmut 2020-10-07 11:07
- statistically significant ≠ clinically relevant Sivakrishna 2020-10-09 10:24
- Problems with low variability Helmut 2020-10-09 15:04
- statistically significant ≠ clinically relevant Sivakrishna 2020-10-09 10:24