AI 中文总结
本文提出通过显式增益函数优化图形多重检验过程，以最大化申办者价值，同时保持家庭错误率控制，并处理设计不确定性及组序贯早期声明奖励。
AI 中文摘要
图形多重检验过程是一种灵活且透明的方式，用于在确认性试验追求多个标签声明时控制家庭错误率，但它们留下了使用哪个图形的问题。在实践中，申办者常常采用固定序列或Holm过程，这些过程可能无法很好地反映试验实际试图实现的目标。我们提出改为选择最大化显式增益函数的图形，该函数规定了每个可能的被拒绝假设集合对申办者的价值：通常在没有通过监管障碍之前价值为零，然后每增加一个声明就增加一个增量。传统的功效标准作为特殊情况被恢复，并且由于搜索限于图形过程，无论选择哪个图形，家庭错误率控制都成立。我们制定了一个结构化的程序，从试验团队的临床、商业和监管成员中引出增益函数。所提出的框架还通过在其合理值上平均性能而不是固定单一假设，来处理设计阶段假设的治疗效果和相关性的不确定性。对于组序贯设计，我们描述了它如何进一步奖励早期声明。五个来自真实和假设制药试验的例子表明，最佳图形取决于试验成功如何定义、设计假设的不确定性程度，以及在组序贯设置中声明何时可以建立。
英文摘要
Graphical multiple testing procedures are a flexible and transparent way to control the family-wise error rate when a confirmatory trial pursues several label claims, but they leave open the question of which graph to use. In practice sponsors often fall back on fixed-sequence or Holm procedures that may poorly reflect what the trial is actually trying to achieve. We propose to instead choose the graph that maximises an explicit gain function, which states what each possible set of rejected hypotheses is worth to the sponsor: typically nothing until a regulatory hurdle is cleared, then an incremental amount for each further claim. Conventional power criteria are recovered as special cases, and because the search is confined to graphical procedures, family-wise error rate control holds whichever graph is selected. We set out a structured procedure for eliciting the gain function from the clinical, commercial, and regulatory members of a trial team. The proposed framework also handles uncertainty in the treatment effects and correlations assumed at the design stage, by averaging performance over their plausible values rather than fixing a single assumption. For group sequential designs, we describe how it can further reward early claims. Five examples, drawn from real and hypothetical pharmaceutical trials, show that the best graph depends on how trial success is defined, how uncertain the design assumptions are, and, in a group sequential setting, when claims can be established.
Comments28 pages, 7 figures. Includes Supporting Information (Sections S1-S3)
