A simple solution to the inadequacy of asymptotic likelihood-based inference for response-adaptive clinical trials

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

Statistical Papers, 2022, vol. 63, issue 1, No 7, 157-180

Abstract: Abstract The present paper discusses drawbacks and limitations of likelihood-based inference in sequential clinical trials for treatment comparisons managed via Response-Adaptive Randomization. Taking into account the most common statistical models for the primary outcome—namely binary, Poisson, exponential and normal data—we derive the conditions under which (i) the classical confidence intervals degenerate and (ii) the Wald test becomes inconsistent and strongly affected by the nuisance parameters, also displaying a non monotonic power. To overcome these drawbacks, we provide a very simple solution that could preserve the fundamental properties of likelihood-based inference. Several illustrative examples and simulation studies are presented in order to confirm the relevance of our results and provide some practical recommendations.

Keywords: Confidence intervals; Ethics; Hypothesis testing; Power; Target allocations; Type-I errors (search for similar items in EconPapers)
Date: 2022
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DOI: 10.1007/s00362-021-01234-3

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