The question of selecting the ‘best’ among different choices is a common problem in statistics. In drug development, our motivating setting, the question becomes, for example, which treatment gives the best response rate. Motivated by recent developments in the theory of context‐dependent information measures, we propose a flexible response‐adaptive experimental design based on a novel criterion governing treatment arm selections which can be used in adaptive experiments with simple (e.g. binary) and complex (e.g. co‐primary, ordinal or nested) end points. It was found that, for specific choices of the context‐dependent measure, the criterion leads to a reliable selection of the correct arm without any parametric or monotonicity assumptions and provides noticeable gains in settings with costly observations. The asymptotic properties of the design are studied for different allocation rules, and the small sample size behaviour is evaluated in simulations in the context of phase II clinical trials with different end points. We compare the proposed design with currently used alternatives and discuss its practical implementation.

中文翻译：

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在临床试验中选择武器的信息理论方法

在不同选择中选择“最佳”的问题是统计中的常见问题。在药物研发中，这是我们的动力所在，例如，问题就变成了哪种治疗方法能产生最佳的反应率。基于上下文相关信息度量理论的最新发展，我们提出了一种基于新颖标准的灵活响应自适应实验设计，该准则控制着治疗臂的选择，可用于简单（例如二进制）和复杂（例如联合）的自适应实验。 -主要，有序或嵌套）端点。结果发现，对于上下文相关度量的特定选择，该准则可以在没有任何参数或单调性假设的情况下可靠地选择正确的分支，并通过昂贵的观察获得明显的收益。研究了针对不同分配规则的设计的渐近性质，并在具有不同终点的II期临床试验的背景下，通过仿真评估了小样本量的行为。我们将建议的设计与当前使用的替代方案进行比较，并讨论其实际实现。