Two‐stage designs versus European scaled average designs in bioequivalence studies for highly variable drugs: Which to choose?

• Published in: Statistics in medicine Vol. 36; no. 30; pp. 4777 - 4788
• Main Authors: Molins, Eduard, Cobo, Erik, Ocaña, Jordi
• Format: Journal Article
• Language: English
• Published: England Wiley Subscription Services, Inc 30.12.2017; John Wiley & Sons
• Subjects: Algorithms; average bioequivalence (ABE); Biostatistics - methods; Clinical trials; Clinical Trials as Topic - methods; Clinical Trials as Topic - statistics & numerical data; Computer Simulation; Cross-Over Studies; Drogues; Drug and Narcotic Control; Drug Approval - legislation & jurisprudence; Drug Approval - statistics & numerical data; Drugs of abuse; Equivalència terapèutica dels medicaments; European Union; Government Agencies; highly variable drugs (HVD); Humans; Medical statistics; Monte Carlo Method; Monte Carlo simulation; Pharmaceuticals; reference scaled average bioequivalence (RSABE); Sample Size; significance level adjustment; Therapeutic Equivalency; Therapeutic equivalency in drugs; two‐stage designs (TSD); United States; United States Food and Drug Administration; reference scaled average bioequivalence (RSABE); average bioequivalence (ABE); highly variable drugs (HVD); significance level adjustment; two-stage designs (TSD)
• ISSN: 0277-6715; 1097-0258
• DOI: 10.1002/sim.7452

The usual approach to determine bioequivalence for highly variable drugs is scaled average bioequivalence, which is based on expanding the limits as a function of the within‐subject variability in the reference formulation. This requires separately estimating this variability and thus using replicated or semireplicated crossover designs. On the other hand, regulations also allow using common 2 × 2 crossover designs based on two‐stage adaptive approaches with sample size reestimation at an interim analysis. The choice between scaled or two‐stage designs is crucial and must be fully described in the protocol. Using Monte Carlo simulations, we show that both methodologies achieve comparable statistical power, though the scaled method usually requires less sample size, but at the expense of each subject being exposed more times to the treatments. With an adequate initial sample size (not too low, eg, 24 subjects), two‐stage methods are a flexible and efficient option to consider: They have enough power (eg, 80%) at the first stage for non‐highly variable drugs, and, if otherwise, they provide the opportunity to step up to a second stage that includes additional subjects.