Making all pairwise comparisons in multi-arm clinical trials without control treatment

TL;DR

This paper introduces efficient, flexible hypothesis testing methods for multi-arm clinical trials without control treatments, improving power and error control over traditional approaches like Bonferroni adjustments.

Contribution

It develops novel hypothesis testing procedures specifically designed for comparing multiple experimental treatments without a control, including extensions for adaptive trial designs.

Findings

Error rate is controlled exactly at the desired level.

Methods outperform standard adjustments like Bonferroni in power.

Applicable to multi-stage adaptive trials and broader contexts.

Abstract

The standard paradigm for confirmatory clinical trials is to compare experimental treatments with a control, for example the standard of care or a placebo. However, it is not always the case that a suitable control exists. Efficient statistical methodology is well studied in the setting of randomised controlled trials. This is not the case if one wishes to compare several experimental with no control arm. We propose hypothesis testing methods suitable for use in such a setting. These methods are efficient, ensuring the error rate is controlled at exactly the desired rate with no conservatism. This in turn yields an improvement in power when compared with standard methods one might otherwise consider using, such as a Bonferroni adjustment. The proposed testing procedure is also highly flexible. We show how it may be extended for use in multi-stage adaptive trials, covering the majority of scenarios in which one might consider the use of such procedures in the clinical trials setting. With such a highly flexible nature, these methods may also be applied more broadly outside of a clinical trials setting.