OBJECTIVES/GOALS: Win ratio (WR) is an increasingly popular composite endpoint in clinical trials. A typical set up in cardiovascular trials is to use death as the first and hospitalization as the second layer. However, the power of WR may be reduced by its strict hierarchical structure. Our study aims to release the oracular hierarchical structure of the standard WR. METHODS/STUDY POPULATION: Addressing the power reduction of WR when treatment effects lie in the subsequent layers, we propose an improved method, Shrinking Coarsened Win Ratio (SCWR), that releases the oracular hierarchical structure of the standard WR approach by adding layers with coarsened thresholds shrinking to zero. A weighted adaptive approach is developed to determine the thresholds in SCWR. We conducted simulations to compare the performance of our improved method and the standard Win Ratio (WR) under different scenarios of follow-up time, association between events, and treatment effect levels. We also illustrate our method by re-analyzing real-world cardiovascular trials. RESULTS/ANTICIPATED RESULTS: First, the developed Shrinking Coarsened Win Ratio (SCWR) method preserves the good statistical properties of the standard WR and has a greater capacity to detect treatment effects on subsequent layer outcomes. Second, the SCWR method outperforms the standard approach under the scenarios in our simulations in terms of gaining higher power. In practice, we expect that SCWR can better detect the treatment effects. Finally, we will offer convenient software tools and clear tutorials for implementing the SCWR method in future studies, which include both unstratified and stratified designs. DISCUSSION/SIGNIFICANCE: The developed SCWR provides a more flexible way of combining the top layer and subsequent layers (e.g., the fatal and non-fatal endpoints) under the hierarchical structure and achieves a higher power in simulation. This nonparametric approach can accommodate different types of outcomes, including time-to-event, continuous, and categorical ones.