Utilizing Win Ratio Approaches and Two-Stage Enrichment Designs for Small-Sized Clinical Trials

TL;DR

This paper explores advanced win ratio methods and two-stage enrichment designs to improve analysis and efficiency in small-sized clinical trials with composite endpoints, especially for rare diseases.

Contribution

It introduces three innovative win ratio methods and integrates them into two-stage enrichment designs with sample size adaptations for better trial efficiency.

Findings

Win ratio methods outperform traditional analyses in certain scenarios.

Two-stage enrichment designs improve trial efficiency for rare diseases.

Proposed methods maintain control of Type I error rate.

Abstract

Conventional methods for analyzing composite endpoints in clinical trials often only focus on the time to the first occurrence of all events in the composite. Therefore, they have inherent limitations because the individual patients' first event can be the outcome of lesser clinical importance. To overcome this limitation, the concept of the win ratio (WR), which accounts for the relative priorities of the components and gives appropriate priority to the more clinically important event, was examined. For example, because mortality has a higher priority than hospitalization, it is reasonable to give a higher priority when obtaining the WR. In this paper, we evaluate three innovative WR methods (stratified matched, stratified unmatched, and unstratified unmatched) for two and multiple components under binary and survival composite endpoints. We compare these methods to traditional ones, including the Cox regression, O'Brien's rank-sum-type test, and the contingency table for controlling study Type I error rate. We also incorporate these approaches into two-stage enrichment designs with the possibility of sample size adaptations to gain efficiency for rare disease studies.