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Response-Adaptive Randomization (RAR) is part of a wider class ofdata-dependent sampling algorithms, for which clinical trials are typicallyused as a motivating application. In that context, patient allocation totreatments is determined by randomization probabilities that change based onthe accrued response data in order to achieve experimental goals. RAR hasreceived abundant theoretical attention from the biostatistical literaturesince the 1930's and has been the subject of numerous debates. In the lastdecade, it has received renewed consideration from the applied andmethodological communities, driven by well-known practical examples and itswidespread use in machine learning. Papers on the subject present differentviews on its usefulness, and these are not easy to reconcile. This work aims toaddress this gap by providing a unified, broad and fresh review ofmethodological and practical issues to consider when debating the use of RAR inclinical trials.

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