A non-parametric U-statistic testing approach for multi-arm clinical trials with multivariate longitudinal data

TL;DR

This paper introduces a non-parametric, multi-arm extension of the LRST for analyzing multivariate longitudinal data in clinical trials, improving power and error control over existing two-arm methods.

Contribution

It develops a novel multi-arm LRST that enables effective comparison of multiple treatment doses against a control in complex longitudinal trials.

Findings

Maintains excellent Type I error control

Offers greater statistical power than two-arm LRST

Validated with real clinical trial data

Abstract

Randomized clinical trials (RCTs) often involve multiple longitudinal primary outcomes to comprehensively assess treatment efficacy. The Longitudinal Rank-Sum Test (LRST), a robust U-statistics-based, non-parametric, rank-based method, effectively controls Type I error and enhances statistical power by leveraging the temporal structure of the data without relying on distributional assumptions. However, the LRST is limited to two-arm comparisons. To address the need for comparing multiple doses against a control group in many RCTs, we extend the LRST to a multi-arm setting. This novel multi-arm LRST provides a flexible and powerful approach for evaluating treatment efficacy across multiple arms and outcomes, with a strong capability for detecting the most effective dose in multi-arm trials. Extensive simulations demonstrate that this method maintains excellent Type I error control while providing greater power compared to the two-arm LRST with multiplicity adjustments. Application to the Bapineuzumab (Bapi) 301 trial further validates the multi-arm LRST's practical utility and robustness, confirming its efficacy in complex clinical trial analyses.