Correlation Matters! Streamlining the Sample Size Procedure with Composite Time-to-event Endpoints

TL;DR

This paper develops refined formulas for sample size calculation in clinical trials using composite time-to-event endpoints with Win Ratio analysis, accounting for endpoint correlations to improve efficiency and accuracy.

Contribution

It introduces new formulas that incorporate endpoint correlation into sample size calculations for Win Ratio-based analyses, enhancing practical applicability.

Findings

Correlation among endpoints significantly impacts sample size requirements.

The formulas improve accuracy over traditional methods assuming independence.

Case studies demonstrate practical benefits in real clinical trial scenarios.

Abstract

Composite endpoints are widely used in cardiovascular clinical trials to improve statistical efficiency while preserving clinical relevance. The Win Ratio (WR) measure and more general frameworks of Win Statistics have emerged as increasingly popular alternatives to traditional time-to-first-event analyses. Although analytic sample size formulas for WR have been developed, they rely on design parameters that are often not straightforward to specify. Consequently, sample size determination in clinical trials with WR as the primary analysis is most often based on simulations, which can be computationally intensive. Moreover, these simulations commonly assume independence among component endpoints, an assumption that may not hold in practice and can lead to misleading power estimates. To address this challenge, we derive refined formulas to calculate the proportions of wins, losses, and ties for multiple prioritized time-to-event endpoints. These formulas rely on familiar design inputs and become directly applicable when integrated with existing sample size methods. We conduct a comprehensive assessment of how correlation among endpoints affects sample size requirements across varying design features. We further demonstrate the role of correlations through two case studies based on the landmark SPRINT and STICH clinical trials to generate further insights.