Should the two-trial paradigm still be the gold standard in drug assessment?

Two significant pivotal trials are usually required for a new drug approval by a regulatory agency. This standard requirement is known as the two-trial paradigm. However, several authors have questioned why we need exactly two pivotal trials, what statistical error the regulators are trying to protect against, and potential alternative approaches. Therefore, it is important to investigate these questions to better understand the regulatory decision-making in the assessment of drugs' effectiveness. It is common that two identically designed trials are run solely to adhere to the two-trial rule. Previous work showed that combining the data from the two trials into a single trial (one-trial paradigm) would increase the power while ensuring the same level of type I error protection as the two-trial paradigm. However, this is true only under a specific scenario and there is little investigation on the type I error protection over the whole null region. In this article, we compare the two paradigms by considering scenarios in which the two trials are conducted in identical or different populations as well as with equal or unequal size. With identical populations, the results show that a single trial provides better type I error protection and higher power. Conversely, with different populations, although the one-trial rule is more powerful in some cases, it does not always protect against the type I error. Hence, there is the need for appropriate flexibility around the two-trial paradigm and the appropriate approach should be chosen based on the questions we are interested in.

Automated summary

This article discusses the standard requirement of two pivotal trials for drug approval and the limitations of the two-trial paradigm. It suggests alternative approaches and compares the two paradigms in terms of error protection and power. The results show that a single trial provides better error protection and power in the same population, but may not always prevent errors in different populations.