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Response adaptive intervention allocation in stepped-wedge cluster randomized trials

Lookup NU author(s): Dr Michael GraylingORCiD, Professor James WasonORCiD



This work is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0).


© 2022 The Authors. Statistics in Medicine published by John Wiley & Sons Ltd. Background: Stepped-wedge cluster randomized trial (SW-CRT) designs are often used when there is a desire to provide an intervention to all enrolled clusters, because of a belief that it will be effective. However, given there should be equipoise at trial commencement, there has been discussion around whether a pre-trial decision to provide the intervention to all clusters is appropriate. In pharmaceutical drug development, a solution to a similar desire to provide more patients with an effective treatment is to use a response adaptive (RA) design. Methods: We introduce a way in which RA design could be incorporated in an SW-CRT, permitting modification of the intervention allocation during the trial. The proposed framework explicitly permits a balance to be sought between power and patient benefit considerations. A simulation study evaluates the methodology. Results: In one scenario, for one particular RA design, the proportion of cluster-periods spent in the intervention condition was observed to increase from 32.2% to 67.9% as the intervention effect was increased. A cost of this was a 6.2% power drop compared to a design that maximized power by fixing the proportion of time in the intervention condition at 45.0%, regardless of the intervention effect. Conclusions: An RA approach may be most applicable to settings for which the intervention has substantial individual or societal benefit considerations, potentially in combination with notable safety concerns. In such a setting, the proposed methodology may routinely provide the desired adaptability of the roll-out speed, with only a small cost to the study's power.

Publication metadata

Author(s): Grayling MJ, Wason JMS, Villar SS

Publication type: Article

Publication status: Published

Journal: Statistics in Medicine

Year: 2022

Volume: 41

Issue: 6

Pages: 1081-1099

Print publication date: 15/03/2022

Online publication date: 21/01/2022

Acceptance date: 22/12/2021

Date deposited: 03/03/2022

ISSN (print): 0277-6715

ISSN (electronic): 1097-0258

Publisher: John Wiley and Sons Ltd


DOI: 10.1002/sim.9317


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