RESUMO
PURPOSE: A drug is defined as highly variable if its intra-individual coefficient of variation (CV) is greater than or equal to 30%. In such a case, bioequivalence may be assessed by means of methods that take the (high) variability into account. The Scaled Average Bioequivalence (SABE) approach is such a procedure and represents the recommendations of FDA. The aim of this investigation is to compare the performance characteristics of classical group sequential designs (GSD) and fixed design settings for three-period crossover bioequivalence studies with highly variable drugs, where the SABE procedure is utilized. METHODS: Monte Carlo simulations were performed to assess type I error rate, power, and average sample size for GSDs with Pocock's and O'Brien-Fleming's stopping rules and various timings of the interim analysis and for fixed design settings. RESULTS: Based on our investigated scenarios, the GSDs show comparable properties with regard to power and type I error rate as compared to the corresponding fixed designs. However, due to an advantage in average sample size, the most appealing design is Pocock's approach with interim analysis after 50% information fraction. CONCLUSIONS: Due to their favorable performance characteristics, two-stage GSDs are an appealing alternative to fixed sample designs when assessing bioequivalence in highly variable drugs.
