An adaptive strategy for assessing regional consistency in multiregional clinical trials

Unexpected regional difference in treatment effect has been reported in recent multiregional clinical trials (MRCTs). This may cause difficulty in interpreting results and can have regulatory implications such as marketing approvals and/or product labels in various markets. Careful consideration of consistency across regions and appropriate plans to address potential regional difference are necessary at the design stage. However, assessment of consistency in treatment effect is generally not the primary objective, and therefore, when there is no strong a priori reason to expect a regional difference, a MRCT is not usually designed to address the regional consistency. Unexpected regional finding may arise and increase the risk of ambiguous or controversial results at the end of the study.

To mitigate this risk, we propose an adaptive strategy for regional assessment based upon accumulated blinded data.

If review of accumulated blinded data shows unexpectedly severe imbalance in an intrinsic or extrinsic factor, and further assessment indicates that this factor could be a potential effect modifier as supported by biological plausibility or blinded correlation analysis, a stratified regional analysis controlled for this factor may be specified and documented before database lock.

The proposed adaptive strategy can help with the interpretation of unexpected regional finding. A recent trial is used to illustrate the approach.

Even if the imbalanced factor may appear to explain away the regional difference, establishment of causal effect is usually difficult and requires more involved effort.

This approach, by prespecifying the stratified analysis, can reduce the risk of post hoc exaggerated emphases across many possible exploratory analyses and provide greater confidence in the validity of the conclusions. If a causal effect can be established that the apparent regional difference is likely caused by this intrinsic or extrinsic factor, this prespecified analysis can also help guide clinical practice.