Priority-Standardized Net Benefit: A Stage-Normalized Estimand for Hierarchical Composite Endpoints

Abstract

Hierarchical composite endpoints analyzed with win statistics are increasingly used when outcomes differ in clinical importance and hard events are too rare to support a single-component primary endpoint. Their appeal is that the analysis respects a prespecified priority order; their less-stated vulnerability is that standard win summaries aggregate layer-specific information using reach probabilities, the fraction of treated-control pairs still tied at each layer. When upper layers are rare or highly tied, a frequently reached last layer can dominate the composite even if it is lowest priority and more vulnerable to bias or missingness, such as an open-label patient-reported outcome. We propose the Priority-Standardized Net Benefit (PSNB), an estimand that decomposes a hierarchical comparison into stage-conditional net benefits and recombines them with a prespecified priority/credibility charter rather than data-determined reach weights. We identify the methodological gap as across-layer aggregation, not within-layer comparison, and show that fixed weighted win-loss statistics remain mechanically reach-weighted; we develop an influence-function-based estimator and large-sample inference, with a ratio-scale companion (the Priority-Standardized Win Ratio); and we give design tools (a layer influence cap, tipping-point analysis, and charter-envelope sensitivity analysis) usable before unblinding. Simulations confirm nominal type I error, show PSNB is approximately invariant to large changes in reach when stage-conditional effects are held fixed, and show that late-layer bias and missingness sensitivity is governed by the charter rather than the reach distribution. At a sample size where the Win Ratio has about 90% power under broad benefit, PSNB with the baseline charters keeps pace, while power under final-layer-dominated benefit depends on the permitted last-layer weight.

38 pages, 9 figures