No 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.

In recent years, global collaboration has become a conventional strategy for new drug development. To accelerate the development process and to shorten approval time, the design of multi-regional trials incorporates subjects from many countries around the world under the same protocol. After showing the overall efficacy of a drug in all global regions, one can also simultaneously evaluate the possibility of applying the overall trial results to all regions and subsequently support drug registration in each of them. Recently, the trend for simultaneous clinical development in Asian countries being undertaken simultaneously with clinical trials conducted in Europe and the United States has been rapidly rising. In this paper, proposals of statistical consideration to multi-regional trials are provided. More specifically, three aspects are addressed: the definition of the 'Asian region,' the consistency criterion between the 'Asian region' and the overall regions, and the sample size determination for the multi-regional trial.

Traditionally the un-weighted Z-tests, which follow the one-patient-one-vote principle, are standard for comparisons of treatment effects. We discuss two types of weighted Z-tests in this manuscript to incorporate data collected in two (or more) stages or in two (or more) regions. We use the type A weighted Z-test to exemplify the variance spending approach in the first part of this manuscript. This approach has been applied to sample size re-estimation. In the second part of the manuscript, we introduce the type B weighted Z-tests and apply them to the design of bridging studies. The weights in the type A weighted Z-tests are pre-determined, independent of the prior observed data, and controls alpha at the desired level. To the contrary, the weights in the type B weighted Z-tests may depend on the prior observed data; and the type I error rate for the bridging study is usually inflated to a level higher than that of a full-scale study. The choice of the weights provides a simple statistical framework for communication between the regulatory agency and the sponsor. The negotiation process may involve practical constrains and some characteristics of prior studies.