A nested group sequential framework for regional evaluation in global drug development program

ABSTRACT

The primary objective of a multiregional clinical trial (MRCT) is to assess the efficacy of all participating regions and evaluate the probability of applying the overall results to a specific region. The consistency assessment of the target region with the overall results is the most common way of evaluating the efficacy in a specific region. Recently, Huang et al. (2012) proposed an additional trial in the target region to an MRCT to evaluate the efficacy in the target ethnic (TE) population under the framework of simultaneous global drug development program (SGDDP). However, the operating characteristics of this statistical framework were not well considered. Therefore, a nested group sequential program for regional efficacy evaluation is proposed in this paper. It is an extension of Huang’s SGDDP framework and allows interim analysis after MRCT and in the course of local clinical trial (LCT) phase. It is able to well control the family-wise type I error in the program level and enhances the flexibility of the program. In LCT sample size estimation, we introduce virtual trial, which is transformed from the original program by using discounting factor, and an iteration method is employed to calculate the sample size and stopping boundaries of interim analyses. The proposed sample size estimation method is validated in the simulations and the effect of varied weight, effect size of TE population, and design setting is explored. Examples with normal end point, binary end point, and survival end point are shown to illustrate the application of the proposed nested group sequential program.

KEYWORDS:

• Discounting factor
• global drug development
• MRCT
• nested group sequential design
• sample size estimation
• virtual trial

Acknowledgment

We would like to thank the editor and two anonymous referees for their helpful comments.

Funding

This work was partially supported by the research grants (grant numbers 81302509, 81473069, and 81273176) from National Science Foundation of China.

Additional information

Funding

This work was partially supported by the research grants (grant numbers 81302509, 81473069, and 81273176) from National Science Foundation of China.