RSABE for highly variable drugs: when, why and how it is calculated

Reference-scaled average bioequivalence lets limits widen with the reference product's own variability. A concise walkthrough of the FDA approach, the design it needs and how it differs from EMA's ABEL.

For highly variable drugs (within-subject CV of roughly 30% or more), fixed 80.00–125.00% limits can demand impractically large studies, even when test and reference are equally good. Reference-scaled approaches address this by scaling the acceptance limit to the variability of the reference product itself.

When the FDA approach applies

Under the FDA's RSABE method, scaling is used when the within-subject standard deviation of the reference, sWR, is at least 0.294 (about 30% CV). Below that, standard average bioequivalence applies.

The design it needs

Estimating sWR requires the reference to be given at least twice to each subject, so RSABE studies use a replicate design:

  • Partial replicate (three-period, e.g. TRR / RTR / RRT)
  • Full replicate (four-period, e.g. TRTR / RTRT)

The criterion

The test is based on the linearised criterion

(μT − μR)² − θ · σ²WR ≤ 0, where θ = (ln 1.25)² / 0.25² ≈ 0.797

Bioequivalence is concluded when both conditions hold:

  1. The 95% upper confidence bound of the criterion (Howe's approximation) is ≤ 0.
  2. The point estimate of the geometric mean ratio lies within 80.00–125.00%.

How EMA differs (ABEL)

FDA RSABEEMA ABEL
TriggersWR ≥ 0.294CVWR > 30%, clinically justified
ParametersCmax and AUCCmax only (AUC by exception)
LimitsImplied by scaled criterionWidened 90% CI limits, capped at 69.84–143.19% (CV 50%)
Point estimate80.00–125.00%80.00–125.00%

Practical tips

  • Pre-specify the method, the software and handling of outliers in the SAP before unblinding.
  • Check that the reference variability is not inflated by a few subjects; report it with and without them.
  • Validate the mixed-model or within-subject computation against a published reference dataset.
Found this useful?Share it with your team or network.

Need help applying this to your study?

Our statisticians, programmers and quality specialists can review it with you.