Recently, a new testing approach for response-adaptive clinical trials was proposed based on the allocation probabilities (AP) rather than the outcome data. While original work on the AP test focused on binary and normal endpoints and demonstrated that significant efficiency gains are possible, many critical questions remain open regarding its practical implementation and upper limits. In this work, rather than simply proposing novel statistics, we seek to understand the maximum gain that can be obtained with the AP test by optimizing how these probabilities are used to define the test statistic. We expand the method's practical utility by applying it to survival endpoints (exponential distributions) and introducing a rigorous strategy for selecting the null hypothesis to properly calibrate type I error. Our simulation studies reveal that by optimizing the functional form of the AP test, investigators can achieve a substantial increase in power, approaching the theoretical maximum, without sacrificing the patient outcome goals of the design. Furthermore, we explicitly compare the method to a standard Bayesian decision rule, finding that the optimized AP test significantly outperforms traditional frequentist tests while maintaining strict error control. This work provides a missing practical framework for implementing robust and optimized AP tests in complex response-adaptive settings.

翻译：摘要：近期，基于分配概率而非结果数据，提出了一种针对响应自适应临床试验的新型检验方法。尽管关于分配概率检验的原始研究聚焦于二元和正态终点，并证明了其可实现显著的效率提升，但其实际实施和上限仍存在许多关键问题。在本研究中，我们并非简单提出新的统计量，而是通过优化分配概率用于定义检验统计量的方式，试图理解分配概率检验所能获得的最大增益。我们通过将该方法应用于生存终点（指数分布），并引入严谨的策略来选择原假设以正确校准第一类错误，扩展了其实用性。模拟研究表明，通过优化分配概率检验的函数形式，研究者能够在实现接近理论最大值的功效显著提升的同时，不牺牲设计中对患者结局的目标。此外，我们将该方法与标准贝叶斯决策规则进行了明确比较，发现优化后的分配概率检验在保持严格错误控制的同时，显著优于传统频率学派检验。本研究为在复杂响应自适应场景中实施稳健且优化的分配概率检验提供了缺失的实用框架。