Interpretational challenges of the Win Ratio in analyzing Hierarchical Composite Endpoints in Chronic Kidney Disease

Win statistics based methods have gained traction as a method for analyzing Hierarchical Composite Endpoints (HCEs) in randomized clinical trials, particularly in cardiovascular and kidney disease research. HCEs offer several key advantages, including increased statistical power, mitigation of competing risks, and hierarchical ranking of clinical outcomes. While, as summary measures, the win ratio (WR) along with the Net Benefit (NB) and the Win Odds (WO) provide a structured approach to analyzing HCEs, several concerns regarding their interpretability remain. In this paper, we present known issues with the WR using simple examples designed to explore the implications for the clinical interpretability of the treatment effect measure in the chronic kidney disease setting. Specifically, we discuss the challenge of defining an appropriate estimand in the context of HCEs using the WR, the difficulties in formulating a relevant causal question underlying the WR, and the dependency of the WR on the variance of its components, which complicates its role as an effect measure. Additionally, we highlight the non-collapsibility and non-transitivity of the WR, further complicating its interpretation. While the WR remains a valuable tool in clinical trials, its inherent limitations must be acknowledged to ensure its proper use in regulatory and clinical decision-making.