A Novel Longitudinal Rank-Sum Test for Multiple Primary Endpoints in Clinical Trials: Applications to Neurodegenerative Disorders

Neurodegenerative disorders such as Alzheimer’s disease (AD) present a significant global health challenge, characterized by cognitive decline, functional impairment, and other debilitating effects. Current AD clinical trials often assess multiple longitudinal primary endpoints to comprehensively evaluate treatment efficacy. Traditional methods, however, may fail to capture global treatment effects, require larger sample sizes due to multiplicity adjustments, and may not fully use the available longitudinal data. To address these limitations, we introduce the Longitudinal Rank Sum Test (LRST), a novel nonparametric rank-based omnibus test statistic. The LRST enables a comprehensive assessment of treatment efficacy across multiple endpoints and time points without the need for multiplicity adjustments, effectively controlling Type I error while enhancing statistical power. It offers flexibility for various data distributions encountered in AD research and maximizes the utilization of longitudinal data. Simulations across realistic clinical trial scenarios, including those with conflicting treatment effects, and real-data applications demonstrate the LRST’s performance, underscoring its potential as a valuable tool in AD clinical trials.

以阿尔茨海默病（Alzheimer’s disease, AD）为代表的神经退行性疾病，是一类严峻的全球性公共卫生挑战，其特征为认知衰退、功能损害及其他致残性后果。当前阿尔茨海默病的临床试验通常设置多项纵向主要终点，以全面评估治疗疗效。然而传统分析方法往往难以捕捉全局治疗效应，因需进行多重性校正而需要更大的样本量，且无法充分利用现有纵向数据。为解决上述局限，本文提出纵向秩和检验（Longitudinal Rank Sum Test, LRST）——一种新颖的非参数基于秩的综合检验统计量。该检验可在无需开展多重性校正的前提下，实现对多终点、多时间点下治疗疗效的全面评估，在有效控制I类错误（Type I error）的同时提升统计功效。其适配阿尔茨海默病研究中常见的各类数据分布类型，并可最大化利用纵向数据信息。通过针对包含冲突治疗效应场景在内的真实临床试验场景开展模拟研究，结合实际数据集的应用验证，证实了纵向秩和检验的性能表现，凸显其作为阿尔茨海默病临床试验中极具价值的分析工具的应用潜力。