In clinical trials, hypotheses are frequently organized into hierarchically ordered families, requiring specialized testing strategies that account for these structured relationships. Existing gatekeeping methods-including serial, parallel, and tree-structured approaches-provide important solutions but are often either too rigid or insufficiently intuitive to accommodate increasingly complex logical dependencies among hypothesis families. To address these limitations, we propose a novel family-based graphical approach that unifies the derivation and visualization of diverse gatekeeping strategies. In this framework, procedures are represented as directed, weighted graphs, where nodes correspond to hypothesis families. Two simple updating rules govern the allocation of significance levels within families and the propagation of significance levels between them. We establish that the proposed method strongly controls the familywise error rate (FWER) at a pre-specified level. Simulation studies under representative configurations indicate that the proposed procedure achieves performance comparable to hypothesis-level graphical approaches and competitive with the superchain procedure, while providing a simpler and more interpretable family-level representation. Case studies and a real clinical trial application further illustrate its flexibility and practical advantages, making it a powerful tool for managing hierarchically structured multiple testing in clinical research.

翻译：在临床试验中，假设通常被组织成具有层次顺序的族，需要能够解释这些结构化关系的专门检验策略。现有的门控方法——包括串行、并行和树形结构方法——提供了重要解决方案，但往往过于僵化或不够直观，难以适应假设族之间日益复杂的逻辑依赖关系。为解决这些局限性，我们提出了一种新颖的基于族的图形化方法，该方法统一了多种门控策略的推导与可视化。在该框架中，检验过程被表示为有向加权图，其中节点对应假设族。两个简单的更新规则控制族内显著性水平的分配以及族间显著性水平的传播。我们证明了所提出的方法能够在预设水平上强控制族系误差率（FWER）。在代表性配置下的模拟研究表明，所提出的过程达到了与假设级图形化方法相当的性能，并与超级链方法具有竞争力，同时提供了更简单且更易解释的族级表示。案例研究和一项真实临床试验应用进一步展示了其灵活性和实际优势，使其成为管理临床研究中层次结构多重检验的有力工具。