Journal Article

Adaptive randomized trial designs that cannot be dominated by any standard design at the same total sample size

Michael Rosenblum

in Biometrika

Published on behalf of The Biometrika Trust

Volume 102, issue 1, pages 191-202
Published in print March 2015 | ISSN: 0006-3444
Published online December 2014 | e-ISSN: 1464-3510 | DOI: http://dx.doi.org/10.1093/biomet/asu057
Adaptive randomized trial designs that cannot be dominated by any standard design at the same total sample size

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Prior work has shown that the power of adaptive designs with rules for modifying the sample size can always be matched or improved by suitably chosen, standard, group sequential designs. A natural question is whether analogous results hold for other types of adaptive designs. We focus on adaptive enrichment designs, which involve preplanned rules for modifying enrollment criteria based on accrued data in a randomized trial. Such designs often involve multiple hypotheses, e.g., one for the total population and one for a predefined subpopulation, such as those with high disease severity at baseline. We fix the total sample size, and consider overall power, defined as the probability of rejecting at least one false null hypothesis. We present adaptive enrichment designs whose overall power at two alternatives cannot simultaneously be matched by any standard design. In some scenarios there is a substantial gap between the overall power achieved by these adaptive designs and that of any standard design. We also prove that such gains in overall power come at a cost. To attain overall power above what is achievable by certain standard designs, it is necessary to increase power to reject some hypotheses and reduce power to reject others. We demonstrate that adaptive enrichment designs allow certain power trade-offs that are not available when restricting to standard designs.

Keywords: Adaptive enrichment design; Multiple testing procedure; Overall power

Journal Article.  0 words. 

Subjects: Biomathematics and Statistics ; Probability and Statistics

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