Resist the Temptation of Response-Adaptive Randomization.

Bayesian approach frequentist approach platform trials response-adaptive randomization temporal trend

Journal

Clinical infectious diseases : an official publication of the Infectious Diseases Society of America
ISSN: 1537-6591
Titre abrégé: Clin Infect Dis
Pays: United States
ID NLM: 9203213

Informations de publication

Date de publication:
31 12 2020
Historique:
received: 21 11 2019
accepted: 26 03 2020
pubmed: 31 3 2020
medline: 29 4 2021
entrez: 31 3 2020
Statut: ppublish

Résumé

Response-adaptive randomization (RAR) has recently gained popularity in clinical trials. The intent is noble: minimize the number of participants randomized to inferior treatments and increase the amount of information about better treatments. Unfortunately, RAR causes many problems, including (1) bias from temporal trends, (2) inefficiency in treatment effect estimation, (3) volatility in sample-size distributions that can cause a nontrivial proportion of trials to assign more patients to an inferior arm, (4) difficulty of validly analyzing results, and (5) the potential for selection bias and other issues inherent to being unblinded to ongoing results. The problems of RAR are most acute in the very setting for which RAR has been proposed, namely long-duration "platform" trials and infectious disease settings where temporal trends are ubiquitous. Response-adaptive randomization can eliminate the benefits that randomization, the most powerful tool in clinical trials, provides. Use of RAR is discouraged.

Identifiants

pubmed: 32222766
pii: 5813456
doi: 10.1093/cid/ciaa334
pmc: PMC7947972
doi:

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

Pagination

3002-3004

Commentaires et corrections

Type : CommentIn
Type : CommentIn
Type : CommentIn
Type : CommentIn

Informations de copyright

Published by Oxford University Press for the Infectious Diseases Society of America 2020.

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Auteurs

Michael Proschan (M)

Mathematical Statistician, Biostatistics Research Branch, National Institute of Allergy and Infectious Diseases, Rockville, Maryland, USA.

Scott Evans (S)

Department of Biostatistics and Bioinformatics; Director, Biostatistics Center, Milken Institute School of Public Health, George Washington University, Washington, DC, USA.

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Classifications MeSH