A Bayesian Approach to Evaluating Regional Treatment Effect in a Multiregional Trial

Abstract

A multiregional trial, conducted in more than one region under a common protocol, is a promising strategy making valuable medicines available to patients globally without time lag. When evaluating the treatment effect for each local region, one may wish to utilize information from other regions to enhance the statistical power. This work proposes a Bayesian approach to bridging data across different regions in a multiregional trial to get an improved analysis of treatment effect for a local region. The new proposal has the following distinct features: (1) It performs internal bridging in a multiregional trial, with the degree of bridging automatically determined by the interregional variability of the treatment effect across different regions; (2) it usually ensures the consistency of the conclusions from local and global inference when the treatment effect is virtually homogeneous across regions and is found nonsignificant globally; (3) it generally protects against overbridging of the global information for evaluating the treatment effect in a very small region. Formulas for statistical power of the proposed method are provided. We illustrate the utility of the proposed method by two numerical examples reflecting typical issues we may encounter in evaluating regional treatment effect in a multiregional trial.

Key Words:

• Empirical Bayes
• Global clinical studies
• Power
• Sample size allocation

ACKNOWLEDGMENTS

The authors thank two anonymous referees, Dr. H. M. James Hung, and Dr. J. L. Christy Chuang-Stein for their valuable comments on the earlier version of this article. The work of Y.-H. Chen was supported in part by the National Science Council of ROC (NSC 95-2118-M-001-022-MY3).

Notes

^a : for local treatment effect.

^b : One-sided p value from the overall test based on .

^c : One-sided p value from the traditional subgroup analysis based on local data .

*: p Value <0.025.