Introducing the Event-Adjusted Rank Sum (EARS) Test: A Simple Approach to Survival Analysis Independent of Proportional Hazards.

Background: Survival analyses often violate the proportional hazards (PH) assumption, compromising the validity of widely used statistical tests such as the log-rank test and Cox regression. To address this limitation, we introduce the event-adjusted rank sum (EARS) test, a nonparametric method designed to provide robust time-to-event data analysis without relying on PH. Materials and Methods: The EARS test adjusts survival times by dividing each event time by the proportion of events within its respective group. These adjusted survival times are then compared using the Kruskal-Wallis test. To account for censoring, the resulting Results: In simulation studies, the EARS and log-rank tests agreed in 96.6% of cases. Under the null hypothesis, the EARS test demonstrated a Type I error rate of 2% across two to five groups, slightly higher than the log-rank test's 1%. Power analyses revealed that EARS detected true differences in 63% of cases compared to 68% for the log-rank test. In 1000 datasets violating the PH assumption, EARS identified significant differences in 94.4% of cases versus 98.0% for RMST, with both methods agreeing 96.4% of the time on null hypothesis rejection. Analyses of clinical cohorts further confirmed the reliability of the EARS test, showing consistent alignment with established tests in most scenarios. Conclusion: The EARS test offers a simple, nonparametric alternative for survival analysis that remains reliable across diverse conditions and varying censoring distributions. Its accessibility and robust performance make it a valuable tool for researchers and clinicians, especially in settings where the PH assumption is violated or advanced statistical software is unavailable. An R package implementing the EARS test is openly available.