All Times EDT
Keywords: Adaptive design, Bioequivalence, Sample Size Re-estimation
A comparative clinical endpoint bioequivalence (BE) study is often used to establish bioequivalence (BE) between a locally acting generic drug (T) and reference drug (R), where efficacy needs to be established for T vs. P and R vs. P; at the same time, equivalence needs to be established for T vs. R. During study design, accurate estimate of parameters (e.g. variance, treatment difference) is essential in determining an adequate sample size to attain sufficient power. However, these parameters may be under or over-estimated due to various reasons, resulting in either insufficient power or unnecessary large sample size. In this work, we propose a novel two-stage adaptive comparative clinical endpoint BE study with sample size re-estimation and optimized allocation ratio based on unblinded/comparative interim analysis, which is an improvement over the current literature. For example, our proposed method improves over Maurer’s 2018 method in that the allocation ratio of sample size in different treatment groups (i.e., T:R:P) is optimized in Stage 2 based on the observed data in Stage 1, which was not done in any of the previous adaptive design methods for BE studies. Simulation results show that our proposed method controls Type 1 error rate under all simulated scenarios. When a fixed design over-estimates the sample size due to over-estimation of variance or treatment difference, our proposed method reduces the average total sample size and optimizes the allocation ratio while achieving the desired power, which cuts cost for generic sponsors; When a fixed design has a low power due to under-estimation of variance or treatment difference, our proposed method optimizes the allocation ratio to attain the targeted power with a minimum total sample size, which helps generic applicants optimize the study design when there is uncertainty, and cut cost and make clinical endpoint BE studies more affordable and efficient.