Journal
STATISTICAL METHODS IN MEDICAL RESEARCH
Volume 32, Issue 6, Pages 1193-1202Publisher
SAGE PUBLICATIONS LTD
DOI: 10.1177/09622802231167437
Keywords
Conditional invariance principle; multiple testing; power; type I error rate
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Response-adaptive randomization adjusts treatment allocation probabilities based on previously observed response data. This article proposes an improved method that guarantees non-negative weights for each block of data and provides a substantial power advantage in practice, especially when patients are allocated in blocks using response-adaptive randomization.
Response-adaptive randomization allows the probabilities of allocating patients to treatments in a clinical trial to change based on the previously observed response data, in order to achieve different experimental goals. One concern over the use of such designs in practice, particularly from a regulatory viewpoint, is controlling the type I error rate. To address this, Robertson and Wason (Biometrics, 2019) proposed methodology that guarantees familywise error rate control for a large class of response-adaptive designs by re-weighting the usual z -test statistic. In this article, we propose an improvement of their method that is conceptually simpler, in the context where patients are allocated to the experimental treatment arms in a trial in blocks (i.e. groups) using response-adaptive randomization. We show the modified method guarantees that there will never be negative weights for the contribution of each block of data to the adjusted test statistics, and can also provide a substantial power advantage in practice.
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