Gain-function optimisation of graphical multiple testing procedures for confirmatory clinical trials
确证性临床试验中图形多重检验程序的收益函数优化AI 解读 ↓
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确证性临床试验中图形多重检验程序的收益函数优化
- 研究问题
- 确证性试验追求多个适应证或标签声明时，如何选择体现实际成功标准的多重检验图，并纳入设计参数不确定性和提前获批价值？
- 方法与思路
- 作者为各种拒绝假设组合设定收益，在图形检验程序中优化预期收益，组织团队提取收益函数，并对效应与相关性可能取值平均评估。
- 材料陈述的贡献
- 摘要称常见功效标准是该框架的特例，图形程序保持族错误率控制；五个示例表明最优图取决于成功定义、设计不确定性及声明时间。
- 阅读关注点
- 应核对收益函数的提取方式、图的选择是否限于设计阶段，以及搜索算法、参数不确定性的权重和组序贯设计中的错误率控制条件。
- 阅读边界
- 这是 arXiv 预印本，同行评审或期刊接收未核实；摘要未说明完整优化与控制条件，不能将设计阶段的保证直接用于事后按结果选图。
原文摘要
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.