Bioequivalence and Bioavailability Forum

Dear all,

I stumbled across this (goody ?):

An adaptive trial design for testing the
bioequivalence of generics of highly
variable drugs

Seemingly¹ a well-known framework² is followed with Pocock’s \(\small{\alpha_\text{adj}=0.0294}\) (for superiority – the correct one for equivalence is \(\small{0.0304}\)…).
I don’t have the paper yet (behind a paywall) but I have some doubts that the method is well thought out:

• \(\small{\alpha_\text{adj}=0.0294}\) in the two-stage approach for 2×2×2 designs (Method B) was a mere lucky punch and does not inflate the Type I Error only for an assumed T/R-ratio of 0.95 and target power of 80%. Any other combination requires other adjustments. Lots of papers…
  Did the authors believe (‼) that Pocock’s \(\small{{\color{Red}{0.0294}}}\) is a ‘natural constant’ which is applicable in any adaptive design?
  
• Assuming a T/R-ratio of 0.95 for HVD(P)s is courageous. A more conservative 0.90 is recommended³ and hence, also the default in the reference-scaling functions of the -package PowerTOST.
  
• The authors’ -package adaptIVPT employs only 1,000 simulations by default (for T/R 0.95 and target power 80%). It is pretty fast because some parts are using C++ code and it supports multiple cores. Furthermore, \(\small{s_\text{wT}\neq s_\text{wR}}\) is supported.
  RSABE itself is a framework (if \(\small{s_\text{wR}<0.294}\) → ABE; if \(\small{s_\text{wR}\geq0.294}\) → scaling and \(\small{PE\in\left\{0.8000-1.2500\right\}}\)). At least 100,00 simulations are required to obtain a stable result in the function sampleN.RSABE().

To summarize:

• Simulation-based adaptive designs require exploration of a reasonable narrow grid of stage 1 sample sizes and CV for an assumed T/R-ratio and target power. Simulations have to be performed under the null hypothesis. Since the convergence under the null is poor, 10⁶ simulations have to be performed. Together with the 100,000 needed for the sample size, one would need 10¹¹ simulations for every grid point.
  
• Under these conditions an \(\small{\alpha_\text{adj}}\) has to be found which maintains the empirical Type I Error.
  
• Since that was obviously not done, with \(\small{\alpha_\text{adj}=0.0294}\) the Type I Error possibly will be inflated. Furthermore, RSABE itself inflates the Type I Error if \(\small{s_\text{wR}<0.294}\), a fact the FDA prefers to ignore.⁴
  BTW, inspecting the source code of adaptIVPT I didn’t find 0.0294 anywhere. Strange.
  
• IMHO, the method is questionable at least.

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1. Lim D, Rantou E, Kim J, Choi S, Choi NH, Grosser S. Adaptive designs for IVPT data with mixed scaled average bioequivalence. Pharm Stat. 2023; 22(6): 1116–34. doi:10.1002/pst.2333.
   
2. Potvin D, DiLiberti CE, Hauck WW, Parr AF, Schuirmann DJ, Smith RA. Sequential design approaches for bioequivalence studies with crossover designs. Pharm Stat. 2008; 7(4): 245–62. doi:10.1002/pst.294. Open access.
   
3. Tóthfalusi L, Endrényi L. Sample Sizes for Designing Bioequivalence Studies for Highly Variable Drugs. J Pharm Phar­ma­ceut Sci. 2012; 15(1): 73–84. doi:10.18433/J3Z88F. Open access.
   
4. Schütz H, Labes D, Wolfsegger MJ. Critical Remarks on Reference-Scaled Average Bioequivalence. J Pharm Pharmaceut Sci. 2022; 25: 285–96. doi:10.18433/jpps32892. Open access.