Rare disease trials face unique statistical challenges due to limited patient populations and heterogeneous clinical manifestations among patients. Multiple endpoints are often necessary to comprehensively capture treatment benefits. A global test is an approach for evaluating whether a treatment has any beneficial effect across multiple endpoints. We propose a new global test based on a weighted composite endpoint. The proposed global test employs shrinkage-based cross-validated targeted maximum likelihood estimation (CV-TMLE) to learn data-adaptive weights that maximize power while maintaining Type I error control. Shrinkage can be tailored to incorporate existing domain knowledge, such as anticipated relative effect sizes. In simulation studies designed to reflect real rare disease trial settings, the proposed procedure demonstrated improved power over standard multiplicity adjustments and classical global tests (such as the O'Brien test), while maintaining nominal Type I error, when effects are heterogeneous across endpoints. The proposed method simultaneously learns an optimal weighted composite outcome and provides an unbiased and efficient targeted maximum likelihood estimator (TMLE) for the average treatment effect (ATE) on that weighted outcome, with valid inference taking into account that the ATE is data dependent.

翻译：罕见疾病试验因受限于有限的患者群体及患者间异质性临床表现，常面临独特的统计学挑战。为全面捕捉治疗获益，往往需要采用多个终点指标。全局检验是一种评估治疗措施是否对多个终点具有显著疗效的方法。本文提出一种基于加权复合终点的全新全局检验方法。该方法采用基于收缩的交叉验证靶向最大似然估计（CV-TMLE）学习数据自适应权重，在控制I类错误的同时最大化检验效能。收缩机制可根据现有领域知识进行定制化设计，例如纳入预期效应量相对大小。在模拟真实罕见疾病试验场景的仿真研究中，当不同终点间效应存在异质性时，本方法相较于标准多重性校正方法及经典全局检验（如O'Brien检验），在维持名义I类错误率的条件下展现出更优的检验效能。该方法可同步学习最优加权复合结局指标，并为该加权结局的平均处理效应（ATE）提供无偏高效的靶向最大似然估计（TMLE），其统计推断过程充分考量了ATE的数据依赖性特征。