Power and sample size calculation for multivariate longitudinal trials using the longitudinal rank sum test

TL;DR

This paper develops a methodology for calculating power and sample size for the Longitudinal Rank Sum Test in multivariate longitudinal trials, aiding efficient clinical trial design for complex neurodegenerative diseases.

Contribution

It introduces a novel approach for power and sample size estimation tailored to the LRST, combining theoretical, asymptotic, and practical methods validated through simulations and real data.

Findings

Accurate power and sample size estimation methods for LRST.

Validation through simulations shows high accuracy.

Application to Alzheimer's and Parkinson's trials demonstrates practical utility.

Abstract

Neurodegenerative diseases such as Alzheimer's and Parkinson's often exhibit complex, multivariate longitudinal outcomes that require advanced statistical methods to comprehensively evaluate treatment efficacy. The Longitudinal Rank Sum Test (LRST) offers a nonparametric framework to assess global treatment effects across multiple longitudinal endpoints without requiring multiplicity corrections. This study develops a robust methodology for power and sample size estimation specific to the LRST, integrating theoretical derivations, asymptotic properties, and practical estimation techniques. Validation through numerical simulations demonstrates the accuracy of the proposed methods, while real-world applications to clinical trials in Alzheimer's and Parkinson's disease highlight their practical significance. This framework facilitates the design of efficient, well-powered trials, advancing the evaluation of treatments for complex diseases with multivariate longitudinal outcomes.