Single-stage, three-arm, adaptive test strategies for non-inferiority trials with an unstable reference

TL;DR

This paper introduces adaptive, single-stage testing strategies for non-inferiority trials with unstable references, optimizing sample size and error control, and demonstrating practical application with real data.

Contribution

It proposes novel adaptive test strategies that improve efficiency and error control in non-inferiority trials with unstable references, including a data-driven approach.

Findings

The natural, data-driven test strategy minimizes sample sizes.

Family-wise error rate is controlled under specific constraints.

The approach is validated with a real-life data set.

Abstract

For indications where only unstable reference treatments are available and use of placebo is ethically justified, three-arm `gold standard' designs with an experimental, reference and placebo arm are recommended for non-inferiority trials. In such designs, the demonstration of efficacy of the reference or experimental treatment is a requirement. They have the disadvantage that only little can be concluded from the trial if the reference fails to be efficacious. To overcome this, we investigate novel single-stage, adaptive test strategies where non-inferiority is tested only if the reference shows sufficient efficacy and otherwise $\delta$-superiority of the experimental treatment over placebo is tested. With a properly chosen superiority margin, $\delta$-superiority indirectly shows non-inferiority. We optimize the sample size for several decision rules and find that the natural, data driven test strategy, which tests non-inferiority if the reference's efficacy test is significant, leads to the smallest overall and placebo sample sizes. We proof that under specific constraints on the sample sizes, this procedure controls the family-wise error rate. All optimal sample sizes are found to meet this constraint. We finally show how to account for a relevant placebo drop-out rate in an efficient way and apply the new test strategy to a real life data set.