Gain-function optimisation of graphical multiple testing procedures for confirmatory clinical trials

2026-09-17

AI Summary

论文提出通过最大化显式增益函数来优化图形多重检验过程，以解决临床试验中选择合适图形的问题，从而更好地反映试验目标。

Abstract

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.

Problem

• graphical multiple testing procedures
• family-wise error rate
• confirmatory clinical trials

Innovation

• gain function
• graphical multiple testing procedures
• family-wise error rate control
• treatment effects uncertainty
• group sequential designs