New strategies for confirmatory testing of secondary hypotheses on combined data from multiple trials.

Abstract

Background: Pivotal evidence of efficacy of a new drug is typically generated by (at least) two clinical trials which independently provide statistically significant and mutually corroborating evidence of efficacy based on a primary endpoint. In this situation, showing drug effects on clinically important secondary objectives can be demanding in terms of sample size requirements. Statistically efficient methods to power for such endpoints while controlling the Type I error are needed.

Methods: We review existing strategies for establishing claims on important but sample size-intense secondary endpoints. We present new strategies based on combined data from two independent, identically designed and concurrent trials, controlling the Type I error at the submission level. We explain the methodology and provide three case studies.

Results: Different strategies have been used for establishing secondary claims. One new strategy, involving a protocol planned analysis of combined data across trials, and controlling the Type I error at the submission level, is particularly efficient. It has already been successfully used in support of label claims. Regulatory views on this strategy differ.

Conclusions: Inference on combined data across trials is a useful approach for generating pivotal evidence of efficacy for important but sample size-intense secondary endpoints. It requires careful preparation and regulatory discussion.