Average Cause-Specific Hazard: A Censoring-Invariant Measure of Event Burden Under Competing Risks

2026-07-14

AI Summary

In studies with competing risks, conventional incidence measures are susceptible to censoring mechanisms and often fail to accurately reflect the true burden of events. This work proposes the Average Cause-Specific Hazard (ACSH), a novel incidence metric based on survival weighting that does not rely on assumptions about the censoring distribution. The authors develop a nonparametric estimator for ACSH and a corresponding two-sample comparison procedure. ACSH retains the intuitive interpretability of incidence rates while fully eliminating bias induced by censoring, and enables group comparisons without requiring strong modeling assumptions. Simulation studies demonstrate its favorable finite-sample properties, and its application to the CANVAS trial yields robust and interpretable estimates of between-group differences.

Abstract

Competing events are common in clinical and epidemiologic studies, including semi-competing risks in which a terminal event such as death may follow a nonfatal event but also competes with it beforehand. Standard summaries include the cumulative incidence function (CIF) and the incidence rate (IR), defined as the number of observed events divided by observed event-free person-time. With competing events, the naive IR generally depends on the censoring-time distribution unless intensities are constant. We propose the Average Cause-Specific Hazard (ACSH), a survival-weighted rate per event-free person-time that preserves the interpretation of an incidence rate and is defined purely from the event-time distribution, without involving the censoring-time distribution. We develop nonparametric estimation and inference for ACSH and, for two-sample comparisons, introduce ACSH differences and ratios that provide interpretable contrasts without requiring a strong model assumption between two groups. Simulation studies examine the finite-sample performance, and an analysis of the CANVAS trial illustrates the proposed methods.

Problem

• competing risks
• censoring
• incidence rate
• event burden
• cause-specific hazard

Innovation

• Average Cause-Specific Hazard
• competing risks
• censoring-invariant
• nonparametric estimation
• incidence rate