Hazard ratios in clinical research: Interpretation, assumptions, and common pitfalls

Abstract

Hazard ratios (HR) play an important role in analyzing time-to-event data in clinical studies, especially cancer trials that measure survival or time to disease progression. HR compare the likelihood of an event happening in one group versus another at any given time. Interpreting them correctly depends on key assumptions that are often overlooked. This review covers the essentials of HRs, including the Cox model, the proportional hazards assumption, how HRs differ from other risk measures, and real-world oncology examples. It points out frequent mistakes and offers practical advice to improve how HRs are reported and understood in trials, aiding better decisions for patients.