Approaches to Allocate Sample Size Rationally Into Individual Regions for a Multi-regional Trial Under Heterogeneous Effect Size

Abstract

A bridging study defined by ICH E5 is usually conducted in the new region after the test product has been approved for commercial marketing in the original region due to its proven efficacy and safety. However, extensive duplication of clinical evaluation in the new region not only requires valuable development resources but also delay availability of the test product to the needed patients in the new regions. To shorten the drug lag or the time lag for approval, simultaneous drug development, submission, and approval in the world may be desirable. Recently, multi-regional trials have attracted much attention from sponsors as well as regulatory authorities. On September 28, 2007, the Ministry of Health, Labour and Welfare (MHLW) in Japan published the “Basic Principles on Global Clinical Trials” guidance related to the planning and implementation of global clinical studies. The 11th Q & A for the ICH E5 guideline also comments the concept of a multi-regional trial. Both guidelines have established a framework on how to demonstrate the efficacy of a drug in all participating regions while also evaluating the possibility of applying the overall trial results to each region by conducting a multi-regional trial. Kawai et al. (2008) developed an approach to rationalize partitioning the total sample size among the regions so that a high probability of observing a consistent trend under the assumptions of the positive treatment effect and uniform across regions in a confirmatory multi-regional trial. Ko et al. (2010) focused on a specific region and establish statistical criteria for consistency between the region of interest and overall results. The sample size calculation for a specific region was also provided. These methods were based on the assumption that true effect size is uniform across regions. In this article, we address the issue that the treatment effects are different among regions to design a multi-regional trial. The random effect model is employed to deal with the heterogeneous effect size among regions. The test statistic for the overall treatment effect is also established and the consistent trend and the proposed criteria are used to rationalize partition sample size to each region.