A novel longitudinal rank-sum test for multiple primary endpoints in clinical trials: Applications to neurodegenerative disorders

TL;DR

This paper introduces the Longitudinal Rank Sum Test (LRST), a new nonparametric method for evaluating multiple primary endpoints over time in clinical trials, especially for neurodegenerative diseases like Alzheimer's.

Contribution

The paper presents the LRST, a novel omnibus test that captures global treatment effects across multiple endpoints and time points without needing multiplicity adjustments.

Findings

LRST controls Type I error effectively.

LRST improves statistical power over traditional methods.

Demonstrated successful application in real AD trial data.

Abstract

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 exploit multivariate 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 multiplicity adjustments, effectively controlling Type I error while enhancing statistical power. It offers flexibility against various data distributions encountered in AD research and maximizes the utilization of longitudinal data. Extensive simulations and real-data applications demonstrate the LRST's performance, underscoring its potential as a valuable tool in AD clinical trials.