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

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.

Automated summary

The paper discusses the drawbacks and limitations of likelihood-based inference in sequential clinical trials for treatment comparisons managed via Response-Adaptive Randomization. It identifies the conditions under which classical confidence intervals degenerate and the Wald test becomes inconsistent, providing a simple solution to preserve the fundamental properties of likelihood-based inference. Illustrative examples and simulation studies are presented to confirm the relevance of the results and offer practical recommendations.