AI 中文总结
研究针对分层复合终点分析中标准方法的弱点，提出优先级标准化净效益（PSNB）估计量，将分层比较分解为阶段条件净效益并按章程重组，开发相关工具，模拟表明其在控制错误、应对变化及偏差等方面有良好表现。
AI 中文摘要
当结局在临床重要性上存在差异且硬事件极为罕见以至于无法支持单组分主要终点时，使用获胜统计分析的分层复合终点越来越多地被采用。其优势在于分析尊重预先指定的优先级顺序；但其较少被提及的弱点是，标准获胜汇总使用到达概率（每层仍处于平局的治疗组与对照组对的比例）来汇总特定层的信息。当上一层罕见或高度平局时，即使最后一层优先级最低且更容易受到偏差或缺失的影响（如开放标签的患者报告结局），经常到达的最后一层也可能主导复合终点。我们提出了优先级标准化净效益（PSNB），这是一种估计量，它将分层比较分解为阶段条件净效益，并根据预先指定的优先级/可信度章程而非数据确定的到达权重重新组合它们。我们将方法学差距识别为跨层汇总而非层内比较，并表明固定加权的胜负统计在机械上仍由到达权重决定；我们开发了基于影响函数的估计器和大样本推断，以及一个比率尺度的伴随量（优先级标准化胜率）；并且我们给出了在揭盲前可用的设计工具（层影响上限、临界点分析和章程包络敏感性分析）。模拟证实了名义上的 I 型错误，表明当阶段条件效应保持固定时，PSNB 对到达的大变化近似不变，并且表明后期层偏差和缺失敏感性由章程而非到达分布控制。在广泛受益下胜率具有约 90%检验功效的样本量时，具有基线章程的 PSNB 与之相当，而在最后一层主导受益下的功效取决于允许的最后一层权重。
英文摘要
Hierarchical composite endpoints analyzed with win statistics are increasingly used when outcomes differ in clinical importance and hard events are too rare to support a single-component primary endpoint. Their appeal is that the analysis respects a prespecified priority order; their less-stated vulnerability is that standard win summaries aggregate layer-specific information using reach probabilities, the fraction of treated-control pairs still tied at each layer. When upper layers are rare or highly tied, a frequently reached last layer can dominate the composite even if it is lowest priority and more vulnerable to bias or missingness, such as an open-label patient-reported outcome. We propose the Priority-Standardized Net Benefit (PSNB), an estimand that decomposes a hierarchical comparison into stage-conditional net benefits and recombines them with a prespecified priority/credibility charter rather than data-determined reach weights. We identify the methodological gap as across-layer aggregation, not within-layer comparison, and show that fixed weighted win-loss statistics remain mechanically reach-weighted; we develop an influence-function-based estimator and large-sample inference, with a ratio-scale companion (the Priority-Standardized Win Ratio); and we give design tools (a layer influence cap, tipping-point analysis, and charter-envelope sensitivity analysis) usable before unblinding. Simulations confirm nominal type I error, show PSNB is approximately invariant to large changes in reach when stage-conditional effects are held fixed, and show that late-layer bias and missingness sensitivity is governed by the charter rather than the reach distribution. At a sample size where the Win Ratio has about 90% power under broad benefit, PSNB with the baseline charters keeps pace, while power under final-layer-dominated benefit depends on the permitted last-layer weight.
Comments38 pages, 9 figures
