{"title":"winratiotest: A command for implementing the win ratio and stratified win ratio in Stata","authors":"John Gregson, João Pedro Ferreira, Tim Collier","doi":"10.1177/1536867x231196480","DOIUrl":null,"url":null,"abstract":"The win ratio is a statistical method most commonly used for analyzing composite outcomes in clinical trials. Composite outcomes comprise two or more distinct “component” events (for example, myocardial infarction or death) and are typically analyzed using time-to-first-event methods ignoring the relative importance of the component events. When using the win ratio, component events are instead placed into a hierarchy from most to least important; more important components can then be prioritized over less important outcomes (for example, death can be prioritized over myocardial infarction). Furthermore, the win ratio enables outcomes of different types (for example, time-to-event, continuous, binary, ordinal, and repeat events) to be combined. We present winratiotest, a command to implement the win-ratio approach for hierarchical outcomes in a flexible and user-friendly way.","PeriodicalId":3,"journal":{"name":"ACS Applied Electronic Materials","volume":null,"pages":null},"PeriodicalIF":4.3000,"publicationDate":"2023-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"ACS Applied Electronic Materials","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1177/1536867x231196480","RegionNum":3,"RegionCategory":"材料科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ENGINEERING, ELECTRICAL & ELECTRONIC","Score":null,"Total":0}
引用次数: 0
Abstract
The win ratio is a statistical method most commonly used for analyzing composite outcomes in clinical trials. Composite outcomes comprise two or more distinct “component” events (for example, myocardial infarction or death) and are typically analyzed using time-to-first-event methods ignoring the relative importance of the component events. When using the win ratio, component events are instead placed into a hierarchy from most to least important; more important components can then be prioritized over less important outcomes (for example, death can be prioritized over myocardial infarction). Furthermore, the win ratio enables outcomes of different types (for example, time-to-event, continuous, binary, ordinal, and repeat events) to be combined. We present winratiotest, a command to implement the win-ratio approach for hierarchical outcomes in a flexible and user-friendly way.