AI 中文总结
本文提出基于效用函数的框架优化 basket 试验的动态借用，通过比较研究分析优化算法可靠性、效用函数统计性能，证明该类试验无一致最优势检验，需透明优化流程。
AI 中文摘要
Basket 试验是指在同一试验中针对多个亚群研究同一种治疗方案的临床试验，下文将这些亚群称为层。分层队列常见于早期肿瘤学试验（但不限于该领域），在这类试验中，可针对不同肿瘤组织定义的层研究靶向疗法。统计方法层面，可利用信息借用对试验分层，核心思路是：若各层对治疗的响应存在差异，则单独分析各层；若响应率相似，则某层可共享其他层的信息，该方法可在适度控制一类错误膨胀的同时提升检验效能。借用方法可针对潜在结局场景进行调优。本文提出了一种用于调优 basket 试验的框架：定义效用函数以表征检验效能与一类错误率之间的权衡，随后使用优化算法对该效用函数进行优化。我们通过一项预先设定的比较研究对该框架展开考察：研究第一部分从可靠性和效率角度对比了不同优化算法，第二部分在模拟场景下对比了不同效用函数的统计性能。研究表明，基于各层局部检验效能、正确决策数量等指标选择不同效用函数，会使优化后的设计产生不同的借用行为。此外，我们讨论了一个数学反例，该反例证明 basket 试验不存在一致最优势（UMP）检验，从而揭示了 basket 试验的局限性：尽管信息借用可在部分场景下提升检验效能，但不存在适用于所有可能场景的最优设计，因此规划 basket 试验时，透明的优化流程至关重要。
英文摘要
Basket trials are clinical trials in which one treatment is investigated in multiple subpopulations within a single trial. The subpopulations are called strata in the following. Stratified cohorts are typical but not limited to early oncological trials, where targeted therapies can be investigated in different strata defined by tumor tissue. In terms of statistical methodology, the stratification of the trial can be leveraged using information borrowing. The idea is that the strata will be analyzed separately if they respond differently to treatment, but that a stratum may share information from another if their response rates are similar. This can increase power while keeping type-I error inflation moderate. The borrowing methods can be tuned with respect to possible outcome scenarios. In this paper, we provide a framework for tuning basket trials: The compromise between power and type-I error rate is defined by a utility function which is then optimized using optimization algorithms. We investigated this framework by performing a pre-specified comparison study. Part I of the study compared optimization algorithms in terms of reliability and efficiency, part II compared the statistical performance of utility functions in simulated scenarios. This study shows how targeting different utility functions based on measures such as local power in each stratum or the number of correct decisions leads to different borrowing behavior of the optimized design. Furthermore, we discuss a mathematical counterexample which shows that no uniformly most powerful (UMP) test exists for basket trials, thus showing the limitations of basket trials. While information borrowing may result in power gains for some scenarios, there is no design which is optimal for all possible scenarios. Hence, a transparent optimization procedure is crucial when planning basket trials.
Comments47 pages, 18 figures
