Blinded sample size re-estimation in a crossover study

Bioequivalence studies play a pivotal role in drug development by establishing the clinical equivalence of two drug formulations. These studies often utilize crossover designs to facilitate within-subject treatment comparisons, optimizing statistical power with fewer subjects. However, uncertainty regarding the variance of a new drug or formulation during planning presents a challenge for sample size determination. While adaptive designs offer a potential solution, their application in crossover studies is less explored compared to group sequential designs, and many existing adaptive methods require data unblinding during the trial. Only two blinded sample size re-estimation approaches have been developed in crossover settings to date. In this paper, we propose a novel method for blinded within-subject variance estimation at interim analysis and re-estimate the sample size to achieve the desired power. We thoroughly investigate its analytical properties and introduce a refined, unbiased estimator. Through extensive simulation studies, our method shows comparable performance to existing blinded approaches and offers a distinct advantage in scenarios with small treatment differences and large subject variances.