Bioequivalence and Bioavailability Forum

Hi Lucas,

welcome to the forum.

❝ ANVISA […] is now studying the possibility of accepting EMA's method for scaling the BE acceptance interval based on the variability of the reference medication.

Interesting.

❝ So we […] did not manage to reproduce the results of PowerTOST calculations for RSABE. Is a whole different calculus?

Yes, since the method is aggregate (scaling for CV_WR >30% with a 50% cap, ratio with 80–125%) you cannot estimate the sample size directly, but have to use simulations – for some background see the paper* by the two Lászlós. Therefore, PASS (or NQuery Advisor as well) cannot do the job.

❝ […] when I try to calculate a sample size for a full replicate crossover design (TRTR, RTRT) considering a true ratio of 0.95, a target power of 80% and a reference ISCV of 63%, in PASS I got 28 subjects (considering the equivalence limits of 69.84%–143.19%) and in PowerTOST I got 22 subjects.

In PowerTOST (EMA’s method) you have to use the function sampleN.scABEL(), not sampleN.TOST() with the scaled acceptance range.

require(PowerTOST)
sampleN.scABEL(theta0=0.95, CV=0.63, targetpower=0.8, design="2x2x4")
+++++++++++ scaled (widened) ABEL +++++++++++
            Sample size estimation
---------------------------------------------
Study design:  2x2x4 (full replicate)
log-transformed data (multiplicative model)
1e+05 studies simulated.

alpha  = 0.05, target power = 0.8
CVw(T) = 0.63; CVw(R) = 0.63
Null (true) ratio = 0.95
ABE limits / PE constraints = 0.8 ... 1.25
Regulatory settings: EMA
- CVswitch =  0.3, cap on ABEL if CVw(R) > 0.5
- Regulatory constant = 0.76

Sample size search
 n    power
22   0.7312
24   0.7773
26   0.8167

László’s Table A2 gives for CV_WR 65% a sample size of 28.
HVDs/HVDPs are also nasty when it comes to the ratio. I would not suggest to assume a ratio of 95% (it jumps around between studies like crazy). I generally use 90%, which in your case would mean 34 sub­jects.

❝ Should I throw away my PASS?

When it comes to reference-scaling, yes.

PS: Where does your 63% come from? If you have done a fully replicated pilot study (RTRT|TRTR or just three periods RTR|TRT) calculate CV_WT as well. Sometimes the reference is an awful product and the test is substantially better. You’ll get a reward for that (the CI will be narrower). In PowerTOST you can give both CVs as a vector (test’s first). Let’s say CV_WT 45% and CV_WR 63%.

sampleN.scABEL(theta0=0.95, CV=c(0.45, 0.63), targetpower=0.8, design="2x2x4")
+++++++++++ scaled (widened) ABEL +++++++++++
            Sample size estimation
---------------------------------------------
Study design:  2x2x4 (full replicate)
log-transformed data (multiplicative model)
1e+05 studies simulated.

alpha  = 0.05, target power = 0.8
CVw(T) = 0.45; CVw(R) = 0.63
Null (true) ratio = 0.95
ABE limits / PE constraints = 0.8 ... 1.25
Regulatory settings: EMA
- CVswitch =  0.3, cap on ABEL if CVw(R) > 0.5
- Regulatory constant = 0.76

Sample size search
 n    power
16   0.6787
18   0.7496
20   0.8038

---

• Tóthfalusi L, Endrényi L. Sample Sizes for Designing Bioequivalence Studies for Highly Variable Drugs J Pharm Pharmaceut Sci. 2011; 15(1): 73–84. online