Response-adaptive randomization in clinical trials: from myths to practical considerations

TL;DR

This paper reviews Response-Adaptive Randomization (RAR) in clinical trials, discussing its theoretical background, practical considerations, and recent renewed interest, aiming to clarify its benefits and limitations.

Contribution

It provides a comprehensive, unified review of RAR methodologies, practical issues, and debates, bridging theoretical and applied perspectives in clinical trial design.

Findings

RAR has a long-standing theoretical foundation since the 1930s.

Recent practical examples have renewed interest in RAR.

Debates on RAR's usefulness highlight both advantages and limitations.

Abstract

Response-Adaptive Randomization (RAR) is part of a wider class of data-dependent sampling algorithms, for which clinical trials are typically used as a motivating application. In that context, patient allocation to treatments is determined by randomization probabilities that change based on the accrued response data in order to achieve experimental goals. RAR has received abundant theoretical attention from the biostatistical literature since the 1930's and has been the subject of numerous debates. In the last decade, it has received renewed consideration from the applied and methodological communities, driven by well-known practical examples and its widespread use in machine learning. Papers on the subject present different views on its usefulness, and these are not easy to reconcile. This work aims to address this gap by providing a unified, broad and fresh review of methodological and practical issues to consider when debating the use of RAR in clinical trials.