Bioequivalence and Bioavailability Forum

Hi Joystar

❝ i have just joined this forum to learn pharmacokinetic and statistical analysis in Bio-equivalence. i read about power and errors in bioequivalence. i don't understand why the power is 80% not 90 or 70.

Power is the chance of showing bioequivalence under your own assumptions of product performance. It is therefore unrelated to patient's risk.
For this reason, there is no strict regulatory requirement about power. If it is too low (too few subjects) then the study may be futile. If the power is too high you may be including more subjects than necessary for the given purpose.
So, many companies select 80% - this is just an empirical choice based on a tradeoff between chance of success and risk of failure. 90% is also commonly used. 70% is in my opinion slightly more rare as it involves a failure risk of 30%.

Hi Joystar,

adding some points to what ElMaestro already said.
Sample size is estimated – not calculated – because it is based on assumptions (of the T/R-ratio, the expected drop-out rate, the risk of failure the producer is willing to accept). See this presentation;¹ especially the section about sensitivity analysis. Many guidelines suggest a power of 80–90%. If you plan a study with a power of <80% it might be possible that the ethics committee rejects the study (too high risk of failure). I know of only one guideline (from New Zealand) suggesting a lower power in case of high variability:²

7.5.5 Number of subjects
[…] the number should be sufficient […] to provide the necessary discriminatory power (normally ≥80%) to detect the maximum allowable difference (usually ±20%) in C_max, AUC etc.
If the calculated number of subjects appears to be higher than is ethically justifiable, it may be necessary to accept a statistical power which is less than desirable. Normally it is not practical to use more than about 40 subjects in a bioavailability study.

Personally I have serious problems understanding the rationale behind. More than 40 are considered unethical. What if the CV is 70%? Power in a full replicate would be just 38.8%³ – so one has a 61.1% chance to fail and repeat the study in another 40 subjects hoping for a better outcome this time? That’s ethical? I call it ignorant.

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1. Sorry for its title. In the meantime I have learned better.
   Should have been “Sample Size Estimation”.
   
2. Maximum CV (40 subjects, T/R 0.95, 80% power):
   2x2x2 cross-over:  30.6%
partial replicate: 36.0%
full replicate:    44.9%
   
3. That’s like rolling two dice and expecting ten dots in total (p₁ = 11/36) or at least one die shows two dots (p₂ = 3/6); p_1|2 = p₁+p₂ = 0.389… I do like gambling but not if humans are pawns in a game.