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47 Shrinking Coarsened Win Ratio and Testing of Composite Endpoint

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 a...

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Autores principales: Mou, Yunhan, Hummel, Scott, Kyriakides, Tassos, Huang, Yuan
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Cambridge University Press 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10129553/
http://dx.doi.org/10.1017/cts.2023.138
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author Mou, Yunhan
Hummel, Scott
Kyriakides, Tassos
Huang, Yuan
author_facet Mou, Yunhan
Hummel, Scott
Kyriakides, Tassos
Huang, Yuan
author_sort Mou, Yunhan
collection PubMed
description 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.
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spelling pubmed-101295532023-04-26 47 Shrinking Coarsened Win Ratio and Testing of Composite Endpoint Mou, Yunhan Hummel, Scott Kyriakides, Tassos Huang, Yuan J Clin Transl Sci Biostatistics, Epidemiology, and Research Design 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. Cambridge University Press 2023-04-24 /pmc/articles/PMC10129553/ http://dx.doi.org/10.1017/cts.2023.138 Text en © The Association for Clinical and Translational Science 2023 https://creativecommons.org/licenses/by-nc-nd/4.0/This is an Open Access article, distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives licence (https://creativecommons.org/licenses/by-nc-nd/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is unaltered and is properly cited. The written permission of Cambridge University Press must be obtained for commercial re-use or in order to create a derivative work.
spellingShingle Biostatistics, Epidemiology, and Research Design
Mou, Yunhan
Hummel, Scott
Kyriakides, Tassos
Huang, Yuan
47 Shrinking Coarsened Win Ratio and Testing of Composite Endpoint
title 47 Shrinking Coarsened Win Ratio and Testing of Composite Endpoint
title_full 47 Shrinking Coarsened Win Ratio and Testing of Composite Endpoint
title_fullStr 47 Shrinking Coarsened Win Ratio and Testing of Composite Endpoint
title_full_unstemmed 47 Shrinking Coarsened Win Ratio and Testing of Composite Endpoint
title_short 47 Shrinking Coarsened Win Ratio and Testing of Composite Endpoint
title_sort 47 shrinking coarsened win ratio and testing of composite endpoint
topic Biostatistics, Epidemiology, and Research Design
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10129553/
http://dx.doi.org/10.1017/cts.2023.138
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