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.

本文讨论了基于序贯性临床试验中基于似然性推理的局限性和局限性，用于通过反应适应性随机化进行治疗比较。考虑到主要结果的最常见统计模型（即二进制，泊松，指数和正态数据），我们得出了以下条件：（i）经典置信区间退化，（ii）Wald检验变得不一致并受到以下因素的强烈影响令人讨厌的参数，也显示非单调功效。为了克服这些缺点，我们提供了一个非常简单的解决方案，可以保留基于似然推理的基本属性。为了证实我们的结果的相关性并提供一些实用的建议，提出了一些说明性的例子和模拟研究。