Bioequivalence and Bioavailability Forum

Hi libaiyi,

❝ how to write the null hypothesis and alternative hypothesis for BE based on FDA or EMA method?

That's a legitimate question. May sound strange but the hypotheses don’t exist until we have estimated s_wR (the extent of scaling in RSABE and the expanded limits in ABEL are random variables and not fixed like in ABE). Hence, both hypotheses are generated “in face of the data” which may lead to inflation of the type I error.^1–7

RSABE and ABEL are frameworks^a,b where one cannot know beforehand which “path” to follow (see here and there). One can only specify the method and its conditions intended to follow in the protocol.

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1. Endrényi L, Tóthfalusi L. Regulatory Conditions for the Determination of Bio­equivalence of Highly Variable Drugs.
   J Pharm Pharmaceut Sci. 2009;12(1):138–49. free resource.
   
2. Wonnemann M, Frömke C, Koch A. Inflation of the Type I Error: Investigations on Regulatory Recommendations for Bioequivalence of Highly Variable Drugs.
   Pharm Res. 2015;32(1):135–43. doi:10.1007/s11095-014-1450-z.
   
3. Muñoz J, Alcaide D, Ocaña J. Consumer's risk in the EMA and FDA regulatory approaches for bioequivalence in highly variable drugs.
   Stat Med. 2016;35(12):1933–43. doi:10.1002/sim.6834.
   
4. Labes D, Schütz H. Inflation of Type I Error in the Evaluation of Scaled Average Bio­equivalence, and a Method for its Control.
   Pharm Res. 2016;33(11):2805–14. doi:10.1007/s11095-016-2006-1. View-only.
   
5. Tóthfalusi L, Endrényi L. Algorithms for evaluating reference scaled average bio­equivalence: Power, bias, and consumer risk.
   Stat Med. 2017;36(27):4378–90. doi:10.1002/sim.7440.
   
6. Molins E., Cobo E., Ocaña J. Two-stage designs versus European scaled average designs in bioequivalence studies for highly variable drugs: Which to choose?
   Stat Med. 2017;36(30):4777–88. doi:10.1002/sim.7452.
   
7. Knahl SIE, Lang B, Fleischer F, Kieser M. A comparison of group sequential and fixed sample size designs for bioequivalence trials with highly variable drugs.
   Eur J Clin Pharmacol. 2018;74(5):549–59. doi:10.1007/s00228-018-2415-7.

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1. RSABE
   • If s_wR ≥0.294 assess the upper 95% CL of the linearized criterion (mixed effects model for full replicate designs, fixed effects model for partial replicate designs).
     • If ≤0, the GMR has to be within 80.00–125%.
   • If s_wR <0.294 perform ABE with conventional limits (mixed effects model for all designs).
2. ABEL
   • If CV_wR >30% expand the BE-limits based on s_wR. If CV_wR >50% expand the limits based on \(s_{wr}^{*}=\sqrt{log{(0.50^2+1)}}\). Fixed effects model for all designs.
     • If the 90% CI lies entirely within the expanded limits, the GMR has to be within 80.00–125%.
   • If CV_wR ≤30% perform ABE with conventional limits (fixed effects model for all designs).