Multivariate Rank-Based Analysis of Multiple Endpoints in Clinical Trials: A Global Test Approach

TL;DR

This paper introduces a multivariate rank-based global testing method for analyzing multiple endpoints in clinical trials, offering robustness and improved performance over existing approaches.

Contribution

The paper proposes a novel multivariate rank-based global test that directly considers joint rankings of endpoints, improving robustness and power in clinical trial analysis.

Findings

Outperforms existing methods in controlling type I error

Achieves higher statistical power in simulations

Robust against diverse data distributions and censoring mechanisms

Abstract

Clinical trials often involve the assessment of multiple endpoints to comprehensively evaluate the efficacy and safety of interventions. In the work, we consider a global nonparametric testing procedure based on multivariate rank for the analysis of multiple endpoints in clinical trials. Unlike other existing approaches that rely on pairwise comparisons for each individual endpoint, the proposed method directly incorporates the multivariate ranks of the observations. By considering the joint ranking of all endpoints, the proposed approach provides robustness against diverse data distributions and censoring mechanisms commonly encountered in clinical trials. Through extensive simulations, we demonstrate the superior performance of the multivariate rank-based approach in controlling type I error and achieving higher power compared to existing rank-based methods. The simulations illustrate the advantages of leveraging multivariate ranks and highlight the robustness of the approach in various settings. The proposed method offers an effective tool for the analysis of multiple endpoints in clinical trials, enhancing the reliability and efficiency of outcome evaluations.