A new inferential approach for response-adaptive clinical trials: the variance-stabilized bootstrap

Alessandro Baldi Antognini, Marco Novelli () and Maroussa Zagoraiou ()
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Marco Novelli: University of Bologna
Maroussa Zagoraiou: University of Bologna

TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, 2022, vol. 31, issue 1, No 10, 235-254

Abstract: Abstract This paper discusses disadvantages and limitations of the available inferential approaches in sequential clinical trials for treatment comparisons managed via response-adaptive randomization. Then, we propose an inferential methodology for response-adaptive designs which, by exploiting a variance stabilizing transformation into a bootstrap framework, is able to overcome the above-mentioned drawbacks, regardless of the chosen allocation procedure as well as the desired target. We derive the theoretical properties of the suggested proposal, showing its superiority with respect to likelihood, randomization and design-based inferential approaches. Several illustrative examples and simulation studies are provided in order to confirm the relevance of our results.

Keywords: Confidence intervals; Hypothesis testing; Likelihood Inference; Re-randomization test; Variance stabilization; 62F40; 62K99; 62L05 (search for similar items in EconPapers)
Date: 2022
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DOI: 10.1007/s11749-021-00777-9

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