Response-adaptive randomization in clinical trials: myth vs reality
Exploring the academic debates around recent developments within response-adaptive randomization (RAR) including its increased use in machine learning.
Response-adaptive randomization (RAR) is part of a wider class of data-dependent sampling algorithms, for which clinical trials have typically been used as a motivating application. In that context, patient allocation to treatments is defined by using the accrued data on responses to alter randomization probabilities, in order to achieve different experimental goals.
RAR has received abundant theoretical attention within the biostatistics literature since it was first proposed in the 1930's and has been the subject of numerous debates. In the last decade, it has received renewed consideration from the applied community due to some high-profile practical examples and its widespread use in machine learning.
This talk will present a recent review paper, discussing some apparently conflicting views that can be found in the specialised literature, mentioning some of the most recent methodological work and discussing further issues to consider when debating the use of RAR in clinical trials. It will also present the main points that arose from the published discussion pieces and rejoinder.
Speaker
, Programme Leader, MRC Biostatistics Unit, University of Cambridge
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