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，构成了严峻的全球健康挑战，其特征为认知功能下降、机体功能受损及其他衰弱性影响。当前阿尔茨海默病临床试验通常会评估多项纵向主要终点，以全面评价治疗效果。然而传统方法往往无法捕捉整体治疗效应，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）这一新型基于秩次的非参数综合检验统计量。该检验可在无需进行多重性校正的前提下，对多项终点与时间点下的治疗效果展开全面评估，在有效控制一类错误的同时提升统计功效。其可灵活适配阿尔茨海默病研究中遇到的各类数据分布，并最大化利用纵向数据。通过针对包含治疗效应冲突场景在内的真实临床试验场景开展模拟研究，并结合真实数据应用验证，证实了LRST的性能表现，凸显了其作为阿尔茨海默病临床试验中极具价值的工具的潜力。