  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.
