Abstract
Recently, multi-regional clinical trials (MRCTs), which incorporate subjects from many countries/regions around the world under the same protocol, have been widely conducted by many global pharmaceutical companies. The objective of such trials is to accelerate the development process for a drug and shorten the drug’s approval time in key markets. Several statistical methods have been purposed for the design and evaluation of MRCTs, as well as for assessing the consistency of treatment effects across all regions with one primary endpoint. However, in some therapeutic areas (e.g., Alzheimer’s disease), the clinical efficacy of a new treatment may be characterized by a set of possibly correlated endpoints, known as multiple co-primary endpoints. In this paper, we focus on a specific region and establish three statistical criteria for evaluating consistency between the specific region and overall results in MRCTs with multiple co-primary endpoints. More specifically, two of those criteria are used to assess whether the treatment effect in the region of interest is as large as that of the other regions or of the regions overall, while the other criterion is used to assess the consistency of the treatment effect of the specific region achieving a pre-specified threshold. The sample size required for the region of interest can also be evaluated based on these three criteria.
Highlights
Global drug development has attracted much attention from pharmaceutical companies
The design of multi-regional clinical trials (MRCTs) recruiting subjects from many countries around the world under the same protocol has led to a new strategy for drug development
To address the difficulties related to global drug development, in 1998 the International Conference on Harmonization (ICH) published “Ethnic Factors in the Acceptability of Foreign Clinical Data”, known as the E5 guideline
Summary
Global drug development has attracted much attention from pharmaceutical companies. Sample size determination in MRCT with multiple co-primary endpoints possibility of applying the overall trial results to each region.
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