A0577
Title: A novel longitudinal rank-sum test for multiple primary endpoints in clinical trials
Authors: Dhrubajyoti Ghosh - Kennesaw State University (United States) [presenting]
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 evaluate treatment efficacy comprehensively. 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, the longitudinal rank sum test (LRST) is introduced, 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, thereby 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.
