Abstract
Multiregional clinical trials (MRCTs) have become a standard strategy for pharmaceutical product development worldwide. The heterogeneity of regional treatment effects is anticipated in an MRCT. For a two-group comparative study in an MRCT, patient assignments, including regional weights and treatment allocation ratios, are predetermined under the same protocol. In practice, the observed patient assignments at the final analysis stage are often not equal to the predetermined patient assignments, which may impact the accuracy of estimating the overall treatment effect and may lead to a biased estimator. In this study, we use a discrete random effects model (DREM) to account for the heterogeneous treatment effect across regions in an MRCT and propose a bias-adjusted estimator of the overall treatment effect through a naïve estimator conditioned on ancillary statistics based on the observed patient assignments at the final analysis stage in the trial. We also perform power analysis for the overall treatment effect and determine the overall sample size for the bias-adjusted estimator with the DREM. Results of simulation studies are given to illustrate applications of the proposed approach. Finally, we provide an example to demonstrate the implementation of the proposed approach.